Relevant TOCs

IEEE Transactions on Image Processing - new TOC (2018 May 24) [Website]

Minghui Liao;Baoguang Shi;Xiang Bai; "TextBoxes++: A Single-Shot Oriented Scene Text Detector," vol.27(8), pp.3676-3690, Aug. 2018. Scene text detection is an important step of scene text recognition system and also a challenging problem. Different from general object detections, the main challenges of scene text detection lie on arbitrary orientations, small sizes, and significantly variant aspect ratios of text in natural images. In this paper, we present an end-to-end trainable fast scene text detector, named TextBoxes++, which detects arbitrary-oriented scene text with both high accuracy and efficiency in a single network forward pass. No post-processing other than efficient non-maximum suppression is involved. We have evaluated the proposed TextBoxes++ on four public data sets. In all experiments, TextBoxes++ outperforms competing methods in terms of text localization accuracy and runtime. More specifically, TextBoxes++ achieves an f-measure of 0.817 at 11.6 frames/s for 1024 × 1024 ICDAR 2015 incidental text images and an f-measure of 0.5591 at 19.8 frames/s for 768 × 768 COCO-Text images. Furthermore, combined with a text recognizer, TextBoxes++ significantly outperforms the state-of-the-art approaches for word spotting and end-to-end text recognition tasks on popular benchmarks. Code is available at:

Zhenyu Zhang;Chunyan Xu;Jian Yang;Junbin Gao;Zhen Cui; "Progressive Hard-Mining Network for Monocular Depth Estimation," vol.27(8), pp.3691-3702, Aug. 2018. Depth estimation from the monocular RGB image is a challenging task for computer vision due to no reliable cues as the prior knowledge. Most existing monocular depth estimation works including various geometric or network learning methods lack of an effective mechanism to preserve the cross-border details of depth maps, which yet is very important for the performance promotion. In this paper, we propose a novel end-to-end progressive hard-mining network (PHN) framework to address this problem. Specifically, we construct the hard-mining objective function, the intra-scale and inter-scale refinement subnetworks to accurately localize and refine those hard-mining regions. The intra-scale refining block recursively recovers details of depth maps from different semantic features in the same receptive field while the inter-scale block favors a complementary interaction among multi-scale depth cues of different receptive fields. For further reducing the uncertainty of the network, we design a difficulty-ware refinement loss function to guide the depth learning process, which can adaptively focus on mining these hard-regions where accumulated errors easily occur. All three modules collaborate together to progressively reduce the error propagation in the depth learning process, and then, boost the performance of monocular depth estimation to some extent. We conduct comprehensive evaluations on several public benchmark data sets (including NYU Depth V2, KITTI, and Make3D). The experiment results well demonstrate the superiority of our proposed PHN framework over other state of the arts for monocular depth estimation task.

Xiaowei Zhang;Li Cheng;Bo Li;Hai-Miao Hu; "Too Far to See? Not Really!—Pedestrian Detection With Scale-Aware Localization Policy," vol.27(8), pp.3703-3715, Aug. 2018. A major bottleneck of pedestrian detection lies on the sharp performance deterioration in the presence of small-size pedestrians that are relatively far from the camera. Motivated by the observation that pedestrians of disparate spatial scales exhibit distinct visual appearances, we propose in this paper an active pedestrian detector that explicitly operates over multiple-layer neuronal representations of the input still image. More specifically, convolutional neural nets, such as ResNet and faster R-CNNs, are exploited to provide a rich and discriminative hierarchy of feature representations, as well as initial pedestrian proposals. Here each pedestrian observation of distinct size could be best characterized in terms of the ResNet feature representation at a certain layer of the hierarchy. Meanwhile, initial pedestrian proposals are attained by the faster R-CNNs techniques, i.e., region proposal network and follow-up region of interesting pooling layer employed right after the specific ResNet convolutional layer of interest, to produce joint predictions on the bounding-box proposals' locations and categories (i.e., pedestrian or not). This is engaged as an input to our active detector, where for each initial pedestrian proposal, a sequence of coordinate transformation actions is carried out to determine its proper x-y 2D location and the layer of feature representation, or eventually terminated as being background. Empirically our approach is demonstrated to produce overall lower detection errors on widely used benchmarks, and it works particularly well with far-scale pedestrians. For example, compared with 60.51% log-average miss rate of the state-of-the-art MS-CNN for far-scale pedestrians (those below 80 pixels in bounding-box height) of the Caltech benchmark, the miss rate of our approach is 41.85%, with a notable reduction of 18.66%.

Ming Yin;Shengli Xie;Zongze Wu;Yun Zhang;Junbin Gao; "Subspace Clustering via Learning an Adaptive Low-Rank Graph," vol.27(8), pp.3716-3728, Aug. 2018. By using a sparse representation or low-rank representation of data, the graph-based subspace clustering has recently attracted considerable attention in computer vision, given its capability and efficiency in clustering data. However, the graph weights built using the representation coefficients are not the exact ones as the traditional definition is in a deterministic way. The two steps of representation and clustering are conducted in an independent manner, thus an overall optimal result cannot be guaranteed. Furthermore, it is unclear how the clustering performance will be affected by using this graph. For example, the graph parameters, i.e., the weights on edges, have to be artificially pre-specified while it is very difficult to choose the optimum. To this end, in this paper, a novel subspace clustering via learning an adaptive low-rank graph affinity matrix is proposed, where the affinity matrix and the representation coefficients are learned in a unified framework. As such, the pre-computed graph regularizer is effectively obviated and better performance can be achieved. Experimental results on several famous databases demonstrate that the proposed method performs better against the state-of-the-art approaches, in clustering.

Haider Ali;Lavdie Rada;Noor Badshah; "Image Segmentation for Intensity Inhomogeneity in Presence of High Noise," vol.27(8), pp.3729-3738, Aug. 2018. Automated segmentation of fine objects details in a given image is becoming of crucial interest in different imaging fields. In this paper, we propose a new variational level-set model for both global and interactiveselective segmentation tasks, which can deal with intensity inhomogeneity and the presence of noise. The proposed method maintains the same performance on clean and noisy vector-valued images. The model utilizes a combination of locally computed denoising constrained surface and a denoising fidelity term to ensure a fine segmentation of local and global features of a given image. A two-phase level-set formulation has been extended to a multi-phase formulation to successfully segment medical images of the human brain. Comparative experiments with state-of-the-art models show the advantages of the proposed method.

Yeong-Jun Cho;Kuk-Jin Yoon; "PaMM: Pose-Aware Multi-Shot Matching for Improving Person Re-Identification," vol.27(8), pp.3739-3752, Aug. 2018. Person re-identification is the problem of recognizing people across different images or videos with non-overlapping views. Although a significant progress has been made in person re-identification over the last decade, it remains a challenging task because the appearances of people can seem extremely different across diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person re-identification by analyzing camera viewpoints and person poses called pose-aware multi-shot matching. It robustly estimates individual poses and efficiently performs multi-shot matching based on the pose information. The experimental results obtained by using public person re-identification data sets show that the proposed methods outperform the current state-of-the-art methods, and are promising for accomplishing person re-identification under diverse viewpoints and pose variances.

Xiang Zhang;Jiarui Sun;Siwei Ma;Zhouchen Lin;Jian Zhang;Shiqi Wang;Wen Gao; "Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding," vol.27(8), pp.3753-3765, Aug. 2018. Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hindered. It is because of the fact that encoding sparsely distributed coefficients may consume more bits for representing the index of nonzero coefficients. Therefore, introducing an accurate rate constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of Gaussian distributed data bound its entropy, indicating the actual bitrate can be well estimated by its variance. Hence, a globally variance-constrained sparse representation (GVCSR) model is proposed in this paper, where a variance-constrained rate term is introduced to the optimization process. Specifically, we employ the alternating direction method of multipliers (ADMMs) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of them have shown the state-of-the-art rate-distortion performance for image representation. Furthermore, we investigate the potential of applying the GVCSR algorithm in the practical image set compression, where the optimized dictionary is trained to efficiently represent the images captured in similar scenarios by implicitly utilizing inter-image correlations. Experimental results have demonstrated superior rate-distortion performance against the state-of-the-art methods.

Jiaqi Yang;Yang Xiao;Zhiguo Cao; "Toward the Repeatability and Robustness of the Local Reference Frame for 3D Shape Matching: An Evaluation," vol.27(8), pp.3766-3781, Aug. 2018. The local reference frame (LRF), as an independent coordinate system constructed on the local 3D surface, is broadly employed in 3D local feature descriptors. The benefits of the LRF include rotational invariance and full 3D spatial information, thereby greatly boosting the distinctiveness of a 3D feature descriptor. There are numerous LRF methods in the literature; however, no comprehensive study comparing their repeatability and robustness performance under different application scenarios and nuisances has been conducted. This paper evaluates eight state-of-the-art LRF proposals on six benchmarks with different data modalities (e.g., LiDAR, Kinect, and Space Time) and application contexts (e.g., shape retrieval, 3D registration, and 3D object recognition). In addition, the robustness of each LRF to a variety of nuisances, including varying support radii, Gaussian noise, outliers (shot noise), mesh resolution variation, distance to boundary, keypoint localization error, clutter, occlusion, and partial overlap, is assessed. The experimental study also measures the performance under different keypoint detectors, descriptor matching performance when using different LRFs and feature representation combinations, as well as computational efficiency. Considering the evaluation outcomes, we summarize the traits, advantages, and current limitations of the tested LRF methods.

Yunfeng Zhang;Qinglan Fan;Fangxun Bao;Yifang Liu;Caiming Zhang; "Single-Image Super-Resolution Based on Rational Fractal Interpolation," vol.27(8), pp.3782-3797, Aug. 2018. This paper presents a novel single-image super-resolution (SR) procedure, which upscales a given low-resolution (LR) input image to a high-resolution image while preserving the textural and structural information. First, we construct a new type of bivariate rational fractal interpolation model and investigate its analytical properties. This model has different forms of expression with various values of the scaling factors and shape parameters; thus, it can be employed to better describe image features than current interpolation schemes. Furthermore, this model combines the advantages of rational interpolation and fractal interpolation, and its effectiveness is validated through theoretical analysis. Second, we develop a single-image SR algorithm based on the proposed model. The LR input image is divided into texture and non-texture regions, and then, the image is interpolated according to the characteristics of the local structure. Specifically, in the texture region, the scaling factor calculation is the critical step. We present a method to accurately calculate scaling factors based on local fractal analysis. Extensive experiments and comparisons with the other state-of-the-art methods show that our algorithm achieves competitive performance, with finer details and sharper edges.

Xuelong Li;Zhigang Wang;Xiaoqiang Lu; "Video Synopsis in Complex Situations," vol.27(8), pp.3798-3812, Aug. 2018. Video synopsis is an effective technique for surveillance video browsing and storage. However, most of the existing video synopsis approaches are not suitable for complex situations, especially crowded scenes. This is because these approaches heavily depend on the preprocessing results of foreground segmentation and multiple objects tracking, but the preprocessing techniques usually achieve poor performance in crowded scenes. To address this problem, we propose a comprehensive video synopsis approach which can be applied to scenes with drastically varying crowdedness. The proposed approach differs significantly from the existing methods, and has several appealing properties. First, we propose to detect the crowdedness of a given video, then, extract object tubes in sparse periods and extract video clips in crowded periods, respectively. Through such a solution, the poor performance of preprocessing techniques in crowded scenes can be avoided by extracting the whole video frames. Second, we propose a group-partition algorithm which can discovers the relationships among moving objects and alleviates several segmentation and tracking errors. Third, a group-based greedy optimization algorithm is proposed to automatically determine the length of a synopsis video. Besides, we present extensive experiments that demonstrate the effectiveness and efficiency of the proposed approach.

Wenbo Bao;Xiaoyun Zhang;Li Chen;Lianghui Ding;Zhiyong Gao; "High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion," vol.27(8), pp.3813-3826, Aug. 2018. This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

Yongbing Zhang;Tao Shen;Xiangyang Ji;Yun Zhang;Ruiqin Xiong;Qionghai Dai; "Residual Highway Convolutional Neural Networks for in-loop Filtering in HEVC," vol.27(8), pp.3827-3841, Aug. 2018. High efficiency video coding (HEVC) standard achieves half bit-rate reduction while keeping the same quality compared with AVC. However, it still cannot satisfy the demand of higher quality in real applications, especially at low bit rates. To further improve the quality of reconstructed frame while reducing the bitrates, a residual highway convolutional neural network (RHCNN) is proposed in this paper for in-loop filtering in HEVC. The RHCNN is composed of several residual highway units and convolutional layers. In the highway units, there are some paths that could allow unimpeded information across several layers. Moreover, there also exists one identity skip connection (shortcut) from the beginning to the end, which is followed by one small convolutional layer. Without conflicting with deblocking filter (DF) and sample adaptive offset (SAO) filter in HEVC, RHCNN is employed as a high-dimension filter following DF and SAO to enhance the quality of reconstructed frames. To facilitate the real application, we apply the proposed method to I frame, P frame, and B frame, respectively. For obtaining better performance, the entire quantization parameter (QP) range is divided into several QP bands, where a dedicated RHCNN is trained for each QP band. Furthermore, we adopt a progressive training scheme for the RHCNN where the QP band with lower value is used for early training and their weights are used as initial weights for QP band of higher values in a progressive manner. Experimental results demonstrate that the proposed method is able to not only raise the PSNR of reconstructed frame but also prominently reduce the bit-rate compared with HEVC reference software.

William Meiniel;Jean-Christophe Olivo-Marin;Elsa D. Angelini; "Denoising of Microscopy Images: A Review of the State-of-the-Art, and a New Sparsity-Based Method," vol.27(8), pp.3842-3856, Aug. 2018. This paper reviews the state-of-the-art in denoising methods for biological microscopy images and introduces a new and original sparsity-based algorithm. The proposed method combines total variation (TV) spatial regularization, enhancement of low-frequency information, and aggregation of sparse estimators and is able to handle simple and complex types of noise (Gaussian, Poisson, and mixed), without any a priori model and with a single set of parameter values. An extended comparison is also presented, that evaluates the denoising performance of the thirteen (including ours) state-of-the-art denoising methods specifically designed to handle the different types of noises found in bioimaging. Quantitative and qualitative results on synthetic and real images show that the proposed method outperforms the other ones on the majority of the tested scenarios.

Yuankai Qi;Lei Qin;Jian Zhang;Shengping Zhang;Qingming Huang;Ming-Hsuan Yang; "Structure-Aware Local Sparse Coding for Visual Tracking," vol.27(8), pp.3857-3869, Aug. 2018. Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm, which encodes a target candidate using templates with both global and local sparsity constraints. For robust tracking, we show the local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we design an effective template update scheme. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous state-of-the-art methods.

Shahnawaz Ahmed;Miles Hansard;Andrea Cavallaro; "Constrained Optimization for Plane-Based Stereo," vol.27(8), pp.3870-3882, Aug. 2018. Depth and surface normal estimation are crucial components in understanding 3D scene geometry from calibrated stereo images. In this paper, we propose visibility and disparity magnitude constraints for slanted patches in the scene. These constraints can be used to associate geometrically feasible planes with each point in the disparity space. The new constraints are validated in the PatchMatch Stereo framework. We use these new constraints not only for initialization, but also in the local plane refinement step of this iterative algorithm. The proposed constraints increase the probability of estimating correct plane parameters, and lead to an improved 3D reconstruction of the scene. Furthermore, the proposed constrained initialization reduces the number of iterations before convergence to the optimal plane parameters. In addition, as most stereo image pairs are not perfectly rectified, we modify the view propagation process by assigning the plane parameters to the neighbors of the candidate pixel. To update the plane parameters in the plane refinement step, we use a gradient free non-linear optimizer. The benefits of the new initialization, propagation, and refinement schemes are demonstrated.

Siqi Bao;Pei Wang;Tony C. W. Mok;Albert C. S. Chung; "3D Randomized Connection Network With Graph-Based Label Inference," vol.27(8), pp.3883-3892, Aug. 2018. In this paper, a novel 3D deep learning network is proposed for brain magnetic resonance image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional long-short term memory and the 3D convolution are employed as network units to capture the long-term and short-term 3D properties, respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we further introduce an efficient graph-based node selection and label inference method. Experiments have been carried out on two publicly available databases and results demonstrate that the proposed method can obtain competitive performances as compared with the other state-of-the-art methods.

Cheng Deng;Zhaojia Chen;Xianglong Liu;Xinbo Gao;Dacheng Tao; "Triplet-Based Deep Hashing Network for Cross-Modal Retrieval," vol.27(8), pp.3893-3903, Aug. 2018. Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention. In particular, cross-modal hashing has been widely and successfully used in multimedia similarity search applications. However, almost all existing methods employing cross-modal hashing cannot obtain powerful hash codes due to their ignoring the relative similarity between heterogeneous data that contains richer semantic information, leading to unsatisfactory retrieval performance. In this paper, we propose a triplet-based deep hashing (TDH) network for cross-modal retrieval. First, we utilize the triplet labels, which describe the relative relationships among three instances as supervision in order to capture more general semantic correlations between cross-modal instances. We then establish a loss function from the inter-modal view and the intra-modal view to boost the discriminative abilities of the hash codes. Finally, graph regularization is introduced into our proposed TDH method to preserve the original semantic similarity between hash codes in Hamming space. Experimental results show that our proposed method outperforms several state-of-the-art approaches on two popular cross-modal data sets.

Xinghui Dong;Junyu Dong; "The Visual Word Booster: A Spatial Layout of Words Descriptor Exploiting Contour Cues," vol.27(8), pp.3904-3917, Aug. 2018. Although researchers have made efforts to use the spatial information of visual words to obtain better image representations, none of the studies take contour cues into account. Meanwhile, it has been shown that contour cues are important to the perception of imagery in the literature. Inspired by these studies, we propose to use the spatial layout of words (SLoW) to boost visual word based image descriptors by exploiting contour cues. Essentially, the SLoW descriptor utilises contours and incorporates different types of commonly used visual words, including hand-crafted basic contour elements (referred to as contons), textons, and scale-invariant feature transform words, deep convolutional words and a special type of words: local binary pattern codes. Moreover, SLoW features are combined with spatial pyramid matching (SPM) or vector of locally aggregated descriptors (VLAD) features. The SLoW descriptor and its combined versions are tested in different tasks. Our results show that they are superior to, or at least comparable to, their counterparts examined in this paper. In particular, the joint use of the SLoW descriptor boosts the performance of the SPM and VLAD descriptors. We attribute these results to the fact that contour cues are important to human visual perception and, the SLoW descriptor captures not only local image characteristics but also the global spatial layout of these characteristics in a more perceptually consistent way than its counterparts.

Shuai Li;Dinei Florencio;Wanqing Li;Yaqin Zhao;Chris Cook; "A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain," vol.27(8), pp.3918-3930, Aug. 2018. Detecting camouflaged moving foreground objects has been known to be difficult due to the similarity between the foreground objects and the background. Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from under-detection of the camouflaged foreground objects. In this paper, we present a fusion framework to address this problem in the wavelet domain. We first show that the small differences in the image domain can be highlighted in certain wavelet bands. Then the likelihood of each wavelet coefficient being foreground is estimated by formulating foreground and background models for each wavelet band. The proposed framework effectively aggregates the likelihoods from different wavelet bands based on the characteristics of the wavelet transform. Experimental results demonstrated that the proposed method significantly outperformed existing methods in detecting camouflaged foreground objects. Specifically, the average F-measure for the proposed algorithm was 0.87 compared with 0.71 to 0.8 for the other state-of-the-art methods.

Zhihua Che;Xiaosheng Zhuang; "Digital Affine Shear Filter Banks With 2-Layer Structure and Their Applications in Image Processing," vol.27(8), pp.3931-3941, Aug. 2018. Digital affine shear filter banks with 2-layer structure (DAS-2 filter banks) are constructed and are shown to be with the perfect reconstruction property. The implementation of digital affine shear transforms using the transition and subdivision operators are given. The redundancy rate analysis shows that our digital affine shear transforms have redundancy rate no more than 8 and it decreases with respect to the number of directional filters. Numerical experiments on image processing demonstrate the advantages of our DAS-2 filter banks over many other state-of-the-art frame-based transforms. The connection between DAS-2 filter banks and affine shear tight frames with 2-layer structure is established. Characterizations and constructions of affine shear tight frames with 2-layer structure are provided.

Liang Li;Ming Yang;Chunxiang Wang;Bing Wang; "Cubature Split Covariance Intersection Filter-Based Point Set Registration," vol.27(8), pp.3942-3953, Aug. 2018. Point set registration is a basic but still an open problem in numerous computer vision tasks. In general, there are more than one type of error sources for registration, for example, noise, outliers, and false initialization, may exist simultaneously. These errors could influence the registration independently and dependently. Previous works usually test performance under one of the two types of errors at one time, or they do not perform well under some extreme situations with both of the error sources. This paper presents a robust point set registration algorithm under a filtering framework, which aims to be robust under various types of errors simultaneously. The point set registration problem can be cast into a non-linear state space model. We use a split covariance intersection filter (SCIF) to capture the correlation between the state transition and the observation (moving point set). The two above-mentioned types of errors can be represented as dependent and independent parts in the SCIF. The covariance of the two types of errors will be updated every iteration. Meanwhile, the non-linearity of the observation model is approximated by a cubature transformation. First, the recursive cubature split covariance intersection filter is derived based on the non-linear state space model. Then, we use this algorithm to solve the point set registration problem. This algorithm can approximate non-linearity by a third-order term and consider correlations between the process model and the observation model. Compared with other filtering-based methods, this algorithm is more robust and precise. Tests on both public data sets and experiments validate the precision and robustness of this algorithm to outliers and noise. Comparison experiments show that this algorithm outperforms the state-of-the-art point set registration algorithms in certain respects.

Xin Jin;Haixu Han;Qionghai Dai; "Plenoptic Image Coding Using Macropixel-Based Intra Prediction," vol.27(8), pp.3954-3968, Aug. 2018. The plenoptic image in a super high resolution is composed of a number of macropixels recording both spatial and angular light radiance. Based on the analysis of spatial correlations of macropixel structure, this paper proposes a macropixel-based intra prediction method for plenoptic image coding. After applying an invertible image reshaping method to the plenoptic image, the macropixel structures are aligned with the coding unit grids of a block-based video coding standard. The reshaped and regularized image is compressed by the video encoder comprising the proposed macropixel-based intra prediction, which includes three modes: multi-block weighted prediction mode (MWP); co-located single-block prediction mode; and boundary matching-based prediction mode (BMP). In the MWP mode and BMP mode, the predictions are generated by minimizing spatial Euclidean distance and boundary error among the reference samples, respectively, which can fully exploit spatial correlations among the pixels beneath the neighboring microlens. The proposed approach outperforms high-efficiency video coding standard by an average of 47.0% bitrate reduction. Compared with other state-of-the-art methods, such as pseudo-video based on tiling and arrangement method, intra block copy mode, and locally linear embedding-based prediction, it can also achieve 45.0%, 27.7%, and 22.7% bitrate savings on average, respectively.

Adria Ruiz;Ognjen Rudovic;Xavier Binefa;Maja Pantic; "Multi-Instance Dynamic Ordinal Random Fields for Weakly Supervised Facial Behavior Analysis," vol.27(8), pp.3969-3982, Aug. 2018. We propose a multi-instance-learning (MIL) approach for weakly supervised learning problems, where a training set is formed by bags (sets of feature vectors or instances) and only labels at bag-level are provided. Specifically, we consider the multi-instance dynamic-ordinal-regression (MI-DOR) setting, where the instance labels are naturally represented as ordinal variables and bags are structured as temporal sequences. To this end, we propose MI dynamic ordinal random fields (MI-DORF). In this paper, we treat instance-labels as temporally dependent latent variables in an undirected graphical model. Different MIL assumptions are modelled via newly introduced high-order potentials relating bag and instance-labels within the energy function of the model. We also extend our framework to address the partially observed MI-DOR problem, where a subset of instance labels is also available during training. We show on the tasks of weakly supervised facial action unit and pain intensity estimation, that the proposed framework outperforms alternative learning approaches. Furthermore, we show that MI-DORF can be employed to reduce the data annotation efforts in this context by large-scale.

Kai Zhang;Jianle Chen;Li Zhang;Xiang Li;Marta Karczewicz; "Enhanced Cross-Component Linear Model for Chroma Intra-Prediction in Video Coding," vol.27(8), pp.3983-3997, Aug. 2018. Cross-component linear model (CCLM) for chroma intra-prediction is a promising coding tool in the joint exploration model (JEM) developed by the Joint Video Exploration Team (JVET). CCLM assumes a linear correlation between the luma and chroma components in a coding block. With this assumption, the chroma components can be predicted by the linear model (LM) mode, which utilizes the reconstructed neighboring samples to derive parameters of a linear model by linear regression. This paper presents three new methods to further improve the coding efficiency of CCLM. First, we introduce a multi-model CCLM (MM-CCLM) approach, which applies more than one linear model to a coding block. With MM-CCLM, reconstructed neighboring luma and chroma samples of the current block are classified into several groups, and a particular set of linear model parameters is derived for each group. The reconstructed luma samples of the current block are also classified to predict the associated chroma samples with the corresponding linear model. Second, we propose a multi-filter CCLM (MF-CCLM) technique, which allows the encoder to select the optimal down-sampling filter for the luma component with the 4:2:0 color format. Third, we present an LM-angular prediction method, which synthesizes the angular intra-prediction and the MM-CCLM intra-prediction into a new chroma intra-coding mode. Simulation results show that the BD-rate savings of 0.55%, 4.66%, and 5.08% on average for Y, Cb, and Cr components, respectively, are achieved in all intra-configurations with the proposed three methods. MM-CCLM and MF-CCLM have been adopted into the JEM by JVET.

Hossein Talebi;Peyman Milanfar; "NIMA: Neural Image Assessment," vol.27(8), pp.3998-4011, Aug. 2018. Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications, such as evaluating image capture pipelines, storage techniques, and sharing media. Despite the subjective nature of this problem, most existing methods only predict the mean opinion score provided by data sets, such as AVA and TID2013. Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network. Our architecture also has the advantage of being significantly simpler than other methods with comparable performance. Our proposed approach relies on the success (and retraining) of proven, state-of-the-art deep object recognition networks. Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline. All this is done without need for a “golden” reference image, consequently allowing for single-image, semantic- and perceptually-aware, no-reference quality assessment.

Angshuman Paul;Dipti Prasad Mukherjee;Prasun Das;Abhinandan Gangopadhyay;Appa Rao Chintha;Saurabh Kundu; "Improved Random Forest for Classification," vol.27(8), pp.4012-4024, Aug. 2018. We propose an improved random forest classifier that performs classification with a minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of important and unimportant features, we formulate a novel theoretical upper limit on the number of trees to be added to the forest to ensure improvement in classification accuracy. Our algorithm converges with a reduced but important set of features. We prove that further addition of trees or further reduction of features does not improve classification performance. The efficacy of the proposed approach is demonstrated through experiments on benchmark data sets. We further use the proposed classifier to detect mitotic nuclei in the histopathological data sets of breast tissues. We also apply our method on the industrial data set of dual-phase steel microstructures to classify different phases. Results of our method on different data sets show significant reduction in an average classification error compared with a number of competing methods.

Le Yang;Junwei Han;Dingwen Zhang;Nian Liu;Dong Zhang; "Segmentation in Weakly Labeled Videos via a Semantic Ranking and Optical Warping Network," vol.27(8), pp.4025-4037, Aug. 2018. Weakly supervised video object segmentation (WSVOS) focuses on generating pixel-level object masks for videos only tagged with class labels, which is an essential yet challenging task. For WSVOS, the algorithm is just aware of rough category information rather than the concrete object size and location cues, besides it lacks reliable annotated exemplars to learn temporal evolution in the investigated videos. Basically, there are three challenging factors which may influence the performance of WSVOS: foreground object discovery in each frame, coarse object semantic consistency within each video, and fine-grained segmentation smoothness within neighbor frames. In this paper, we establish a semantic ranking and optical warping network to simultaneously solve these three challenges in a unified framework. For the first challenge, we apply the still image saliency detection method and discover the foreground object for each frame via a segmentation network. Due to the huge discrepancies between the image saliency and the video object segmentation, we step further and propose two subnetworks to solve the other two challenges. For the second one, we propose an attentive semantic ranking subnetwork to mine video-level tags, which can learn discriminative features for semantic ranking and lead to semantic consistent segmentation masks. For the third one, we propose an optical flow warping subnetwork to constrain fine-grained segmentation smoothness within neighbor frames, which can suppress the large deformation and thus obtain smooth object boundaries for adjacent frames. Experiments on two benchmark data sets, i.e., DAVIS data set and YouTube-Objects data set, demonstrate the effectiveness of the proposed approach for segmenting out video objects under weak supervision.

Dahua Gao;Xiaolin Wu; "Multispectral Image Restoration via Inter- and Intra-Block Sparse Estimation Based on Physically-Induced Joint Spatiospectral Structures," vol.27(8), pp.4038-4051, Aug. 2018. Existing low-level vision algorithms (e.g., those for superresolution, denoising, and deblurring) were primarily motivated and optimized for precision in spatial domain. However, high precision in spectral domain is of importance for many applications in scientific and technical fields, such as spectral analysis, recognition, and classification. In quest for both high spectral and spatial fidelity, we introduce previously unexplored, physically induced, and joint spatiospectral sparsities to improve existing methods for multispectral image restoration. The bidirectional image formation model is used to reveal that the discontinuities of a multispectral image tend to align spatially across different spectral bands; in other words, the 2D Laplacians of different bands are not only sparse each, but they also agree with one the other in significance positions. Such strongly structured sparsities give rise to a new inter- and intra-block sparse estimation approach. The estimation is performed on 3D spatiospectral sample blocks, rather than on separate 2D patches, per spectral band or per luminance and chrominance component as in current practice. Moreover, intra-block and inter-block sparsity priors are combined via an intra-block <inline-formula> <tex-math notation="LaTeX">$ell _{1,2}$ </tex-math></inline-formula>-norm minimization term and an inter-block low-rank term, strengthening the regularization of the underlying inverse problem. The new approach is tested and evaluated on two concrete applications. The superresolving and denoising multispectral images; its validity and advantages over the current state-of-the-art are established by empirical results.

Bing Su;Xiaoqing Ding;Changsong Liu;Ying Wu; "Heteroscedastic Max–Min Distance Analysis for Dimensionality Reduction," vol.27(8), pp.4052-4065, Aug. 2018. Max–min distance analysis (MMDA) performs dimensionality reduction by maximizing the minimum pairwise distance between classes in the latent subspace under the homoscedastic assumption, which can address the class separation problem caused by the Fisher criterion but is incapable of tackling heteroscedastic data properly. In this paper, we propose two heteroscedastic MMDA (HMMDA) methods to employ the differences of class covariances. Whitened HMMDA extends MMDA by utilizing the Chernoff distance as the separability measure between classes in the whitened space. Orthogonal HMMDA (OHMMDA) incorporates the maximization of the minimal pairwise Chernoff distance and the minimization of class compactness into a trace quotient formulation with an orthogonal constraint of the transformation, which can be solved by bisection search. Two variants of OHMMDA further encode the margin information by using only neighboring samples to construct the intra-class and inter-class scatters. Experiments on several UCI datasets and two face databases demonstrate the effectiveness of the HMMDA methods.

Samuel F. Dodge;Lina J. Karam; "Visual Saliency Prediction Using a Mixture of Deep Neural Networks," vol.27(8), pp.4080-4090, Aug. 2018. Visual saliency models have recently begun to incorporate deep learning to achieve predictive capacity much greater than previous unsupervised methods. However, most existing models predict saliency without explicit knowledge of global scene semantic information. We propose a model (MxSalNet) that incorporates global scene semantic information in addition to local information gathered by a convolutional neural network. Our model is formulated as a mixture of experts. Each expert network is trained to predict saliency for a set of closely related images. The final saliency map is computed as a weighted mixture of the expert networks’ output, with weights determined by a separate gating network. This gating network is guided by global scene information to predict weights. The expert networks and the gating network are trained simultaneously in an end-to-end manner. We show that our mixture formulation leads to improvement in performance over an otherwise identical non-mixture model that does not incorporate global scene information. Additionally, we show that our model achieves better performance than several other visual saliency models.

Lei Xiao;Felix Heide;Wolfgang Heidrich;Bernhard Schölkopf;Michael Hirsch; "Discriminative Transfer Learning for General Image Restoration," vol.27(8), pp.4091-4104, Aug. 2018. Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing tradeoff between image quality and computational efficiency. However, these methods require separate training for each restoration task (e.g., denoising, deblurring, and demosaicing) and problem condition (e.g., noise level of input images). This makes it time-consuming and difficult to encompass all tasks and conditions during training. In this paper, we propose a discriminative transfer learning method that incorporates formal proximal optimization and discriminative learning for general image restoration. The method requires a single-pass discriminative training and allows for reuse across various problems and conditions while achieving an efficiency comparable to previous discriminative approaches. Furthermore, after being trained, our model can be easily transferred to new likelihood terms to solve untrained tasks, or be combined with existing priors to further improve image restoration quality.

Zhihua Ban;Jianguo Liu;Li Cao; "Superpixel Segmentation Using Gaussian Mixture Model," vol.27(8), pp.4105-4117, Aug. 2018. Superpixel segmentation partitions an image into perceptually coherent segments of similar size, namely, superpixels. It is becoming a fundamental preprocessing step for various computer vision tasks because superpixels significantly reduce the number of inputs and provide a meaningful representation for feature extraction. We present a pixel-related Gaussian mixture model (GMM) to segment images into superpixels. GMM is a weighted sum of Gaussian functions, each one corresponding to a superpixel, to describe the density of each pixel represented by a random variable. Different from previously proposed GMMs, our weights are constant, and Gaussian functions in the sums are subsets of all the Gaussian functions, resulting in segments of similar size and an algorithm of linear complexity with respect to the number of pixels. In addition to the linear complexity, our algorithm is inherently parallel and allows fast execution on multi-core systems. During the expectation-maximization iterations of estimating the unknown parameters in the Gaussian functions, we impose two lower bounds to truncate the eigenvalues of the covariance matrices, which enables the proposed algorithm to control the regularity of superpixels. Experiments on a well-known segmentation dataset show that our method can efficiently produce superpixels that adhere to object boundaries better than the current state-of-the-art methods.

Shutao Li;Renwei Dian;Leyuan Fang;José M. Bioucas-Dias; "Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization," vol.27(8), pp.4118-4130, Aug. 2018. Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR-HSI) has attracted increasing interest in recent years. In this paper, we propose a coupled sparse tensor factorization (CSTF)-based approach for fusing such images. In the proposed CSTF method, we consider an HR-HSI as a 3D tensor and redefine the fusion problem as the estimation of a core tensor and dictionaries of the three modes. The high spatial-spectral correlations in the HR-HSI are modeled by incorporating a regularizer, which promotes sparse core tensors. The estimation of the dictionaries and the core tensor are formulated as a coupled tensor factorization of the LR-HSI and of the HR-MSI. Experiments on two remotely sensed HSIs demonstrate the superiority of the proposed CSTF algorithm over the current state-of-the-art HSI-MSI fusion approaches.

Youngjung Kim;Hyungjoo Jung;Dongbo Min;Kwanghoon Sohn; "Deep Monocular Depth Estimation via Integration of Global and Local Predictions," vol.27(8), pp.4131-4144, Aug. 2018. Recent works on machine learning have greatly advanced the accuracy of single image depth estimation. However, the resulting depth images are still over-smoothed and perceptually unsatisfying. This paper casts depth prediction from single image as a parametric learning problem. Specifically, we propose a deep variational model that effectively integrates heterogeneous predictions from two convolutional neural networks (CNNs), named global and local networks. They have contrasting network architecture and are designed to capture the depth information with complementary attributes. These intermediate outputs are then combined in the integration network based on the variational framework. By unrolling the optimization steps of Split Bregman iterations in the integration network, our model can be trained in an end-to-end manner. This enables one to simultaneously learn an efficient parameterization of the CNNs and hyper-parameter in the variational method. Finally, we offer a new data set of 0.22 million RGB-D images captured by Microsoft Kinect v2. Our model generates realistic and discontinuity-preserving depth prediction without involving any low-level segmentation or superpixels. Intensive experiments demonstrate the superiority of the proposed method in a range of RGB-D benchmarks, including both indoor and outdoor scenarios.

Yifan Zuo;Qiang Wu;Jian Zhang;Ping An; "Minimum Spanning Forest With Embedded Edge Inconsistency Measurement Model for Guided Depth Map Enhancement," vol.27(8), pp.4145-4159, Aug. 2018. Guided depth map enhancement based on Markov random field (MRF) normally assumes edge consistency between the color image and the corresponding depth map. Under this assumption, the low-quality depth edges can be refined according to the guidance from the high-quality color image. However, such consistency is not always true, which leads to texture-copying artifacts and blurring depth edges. In addition, the previous MRF-based models always calculate the guidance affinities in the regularization term via a non-structural scheme, which ignores the local structure on the depth map. In this paper, a novel MRF-based method is proposed. It computes these affinities via the distance between pixels in a space consisting of the minimum spanning trees (forest) to better preserve depth edges. Furthermore, inside each minimum spanning tree, the weights of edges are computed based on the explicit edge inconsistency measurement model, which significantly mitigates texture-copying artifacts. To further tolerate the effects caused by noise and better preserve depth edges, a bandwidth adaption scheme is proposed. Our method is evaluated for depth map super-resolution and depth map completion problems on synthetic and real data sets, including Middlebury, ToF-Mark, and NYU. A comprehensive comparison against 16 state-of-the-art methods is carried out. Both qualitative and quantitative evaluations present the improved performances.

IEEE Transactions on Medical Imaging - new TOC (2018 May 24) [Website]

* "Table of contents," vol.37(5), pp.C1-C4, May 2018.* Presents the table of contents for this issue of the publication.

* "IEEE Transactions on Medical Imaging publication information," vol.37(5), pp.C2-C2, May 2018.* Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Yuanwei Li;Chin Pang Ho;Matthieu Toulemonde;Navtej Chahal;Roxy Senior;Meng-Xing Tang; "Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model," vol.37(5), pp.1081-1091, May 2018. Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to data suffering from large intensity variations. In this paper, we demonstrate how to overcome the above limitations of classic RF by presenting a fully automatic segmentation pipeline for myocardial segmentation in full-cycle 2-D MCE data. Specifically, a statistical shape model is used to provide shape prior information that guide the RF segmentation in two ways. First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to refine and constrain the final segmentation to plausible myocardial shapes. We further improve the performance by introducing a bounding box detection algorithm as a preprocessing step in the segmentation pipeline. Our approach on 2-D image is further extended to 2-D+t sequences which ensures temporal consistency in the final sequence segmentations. When evaluated on clinical MCE data sets, our proposed method achieves notable improvement in segmentation accuracy and outperforms other state-of-the-art methods, including the classic RF and its variants, active shape model and image registration.

Tian Mou;Jian Huang;Finbarr O’Sullivan; "The Gamma Characteristic of Reconstructed PET Images: Implications for ROI Analysis," vol.37(5), pp.1092-1102, May 2018. The basic emission process associated with positron emission tomography (PET) imaging is Poisson in nature. Reconstructed images inherit some aspects of this-regional variability is typically proportional to the regional mean. Iterative reconstruction using expectation-maximization (EM), widely used in clinical imaging now, imposes positivity constraints that impact noise properties. This paper is motivated by the analysis of data from a physical phantom study of a PET/CT scanner in routine clinical use. Both traditional filtered back-projection (FBP) and EM reconstructions of the images are considered. FBP images are quite Gaussian, but the EM reconstructions exhibit Gamma-like skewness. The Gamma structure has implications for how reconstructed PET images might be processed statistically. Post-reconstruction inference-model fitting and diagnostics for regions of interest are of particular interest. Although the relevant Gamma parameterization is not within the framework of generalized linear models (GLM), iteratively re-weighted least squares (IRLS) techniques, which are often used to find the maximum likelihood estimates of a GLM, can be adapted for analysis in this setting. This paper highlights the use of a Gamma-based probability transform in producing normalized residuals as model diagnostics. The approach is demonstrated for quality assurance analyses associated with physical phantom studies-recovering estimates of local bias and variance characteristics in an operational scanner. Numerical simulations show that when the Gamma assumption is reasonable, gains in efficiency are obtained. This paper shows that the adaptation of standard analysis methods to accommodate the Gamma structure is straightforward and beneficial.

Satyananda Kashyap;Honghai Zhang;Karan Rao;Milan Sonka; "Learning-Based Cost Functions for 3-D and 4-D Multi-Surface Multi-Object Segmentation of Knee MRI: Data From the Osteoarthritis Initiative," vol.37(5), pp.1103-1113, May 2018. A fully automated knee magnetic resonance imaging (MRI) segmentation method to study osteoarthritis (OA) was developed using a novel hierarchical set of random forests (RF) classifiers to learn the appearance of cartilage regions and their boundaries. A neighborhood approximation forest is used first to provide contextual feature to the second-level RF classifier that also considers local features and produces location-specific costs for the layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) framework. Double-echo steady state MRIs used in this paper originated from the OA Initiative study. Trained on 34 MRIs with varying degrees of OA, the performance of the learning-based method tested on 108 MRIs showed significant reduction in segmentation errors (p <; 0.05) compared with the conventional gradient-based and single-stage RF-learned costs. The 3-D LOGISMOS was extended to longitudinal-3-D (4-D) to simultaneously segment multiple follow-up visits of the same patient. As such, data from all time-points of the temporal sequence contribute information to a single optimal solution that utilizes both spatial 3-D and temporal contexts. 4-D LOGISMOS validation on 108 MRIs from baseline, and 12 month follow-up scans of 54 patients showed significant reduction in segmentation errors (p <; 0.01) compared with 3-D. Finally, the potential of 4-D LOGISMOS was further explored on the same 54 patients using five annual follow-up scans demonstrating a significant improvement of measuring cartilage thickness (p <; 0.01) compared with the sequential 3-D approach.

Yueming Jin;Qi Dou;Hao Chen;Lequan Yu;Jing Qin;Chi-Wing Fu;Pheng-Ann Heng; "SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network," vol.37(5), pp.1114-1126, May 2018. We propose an analysis of surgical videos that is based on a novel recurrent convolutional network (SV-RCNet), specifically for automatic workflow recognition from surgical videos online, which is a key component for developing the context-aware computer-assisted intervention systems. Different from previous methods which harness visual and temporal information separately, the proposed SV-RCNet seamlessly integrates a convolutional neural network (CNN) and a recurrent neural network (RNN) to form a novel recurrent convolutional architecture in order to take full advantages of the complementary information of visual and temporal features learned from surgical videos. We effectively train the SV-RCNet in an end-to-end manner so that the visual representations and sequential dynamics can be jointly optimized in the learning process. In order to produce more discriminative spatio-temporal features, we exploit a deep residual network (ResNet) and a long short term memory (LSTM) network, to extract visual features and temporal dependencies, respectively, and integrate them into the SV-RCNet. Moreover, based on the phase transition-sensitive predictions from the SV-RCNet, we propose a simple yet effective inference scheme, namely the prior knowledge inference (PKI), by leveraging the natural characteristic of surgical video. Such a strategy further improves the consistency of results and largely boosts the recognition performance. Extensive experiments have been conducted with the MICCAI 2016 Modeling and Monitoring of Computer Assisted Interventions Workflow Challenge dataset and Cholec80 dataset to validate SV-RCNet. Our approach not only achieves superior performance on these two datasets but also outperforms the state-of-the-art methods by a significant margin.

Zhiwei Wang;Chaoyue Liu;Danpeng Cheng;Liang Wang;Xin Yang;Kwang-Ting Cheng; "Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network," vol.37(5), pp.1127-1139, May 2018. Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions’ locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

Tao Feng;Jizhe Wang;Youjun Sun;Wentao Zhu;Yun Dong;Hongdi Li; "Self-Gating: An Adaptive Center-of-Mass Approach for Respiratory Gating in PET," vol.37(5), pp.1140-1148, May 2018. The goal is to develop an adaptive center-of-mass (COM)-based approach for device-less respiratory gating of list-mode positron emission tomography (PET) data. Our method contains two steps. The first is to automatically extract an optimized respiratory motion signal from the list-mode data during acquisition. The respiratory motion signal was calculated by tracking the location of COM within a volume of interest (VOI). The signal prominence (SP) was calculated based on Fourier analysis of the signal. The VOI was adaptively optimized to maximize SP. The second step is to automatically correct signal-flipping effects. The sign of the signal was determined based on the assumption that the average patient spends more time during expiration than inspiration. To validate our methods, thirty-one 18F-FDG patient scans were included in this paper. An external device-based signal was used as the gold standard, and the correlation coefficient of the data-driven signal with the device-based signal was measured. Our method successfully extracted respiratory signal from 30 out of 31 datasets. The failure case was due to lack of uptake in the field of view. Moreover, our sign determination method obtained correct results for all scans excluding the failure case. Quantitatively, the proposed signal extraction approach achieved a median correlation of 0.85 with the device-based signal. Gated images using optimized data-driven signal showed improved lesion contrast over static image and were comparable to those using device-based signal. We presented a new data-driven method to automatically extract respiratory motion signal from list-mode PET data by optimizing VOI for COM calculation, as well as determine motion direction from signal asymmetry. Successful application of the proposed method on most clinical datasets and comparison with device-based signal suggests its potential of serving as an alternative to external respiratory monitors.

Ling Dai;Ruogu Fang;Huating Li;Xuhong Hou;Bin Sheng;Qiang Wu;Weiping Jia; "Clinical Report Guided Retinal Microaneurysm Detection With Multi-Sieving Deep Learning," vol.37(5), pp.1149-1161, May 2018. Timely detection and treatment of microaneurysms is a critical step to prevent the development of vision-threatening eye diseases such as diabetic retinopathy. However, detecting microaneurysms in fundus images is a highly challenging task due to the low image contrast, misleading cues of other red lesions, and the large variation of imaging conditions. Existing methods tend to fail in face of the large intra-class variation and small inter-class variations for microaneurysm detection in fundus images. Recently, hybrid text/image mining computer-aided diagnosis systems have emerged to offer a promise of bridging the semantic gap between images and diagnostic information. In this paper, we focus on developing an interleaved deep mining technique to cope intelligently with the unbalanced microaneurysm detection problem. Specifically, we present a clinical report guided multi-sieving convolutional neural network, which leverages a small amount of supervised information in clinical reports to identify the potential microaneurysm regions via the image-to-text mapping in the feature space. These potential microaneurysm regions are then interleaved with fundus image information for multi-sieving deep mining in a highly unbalanced classification problem. Critically, the clinical reports are employed to bridge the semantic gap between low-level image features and high-level diagnostic information. We build an efficient microaneurysm detection framework based on the hybrid text/image interleaving and validate its performance on challenging clinical data sets acquired from diabetic retinopathy patients. Extensive evaluations are carried out in terms of fundus detection and classification. Experimental results show that our framework achieves 99.7% precision and 87.8% recall, comparing favorably with the state-of-the-art algorithms. Integration of expert domain knowledge and image information demonstrates the feasibility of reducing the difficulty of training classifiers under- extremely unbalanced data distributions.

Hailong He;Andreas Buehler;Dmitry Bozhko;Xiaohua Jian;Yaoyao Cui;Vasilis Ntziachristos; "Importance of Ultrawide Bandwidth for Optoacoustic Esophagus Imaging," vol.37(5), pp.1162-1167, May 2018. Optoacoustic (photoacoustic) endoscopy has shown potential to reveal complementary contrast to optical endoscopy methods, indicating clinical relevance. However operational parameters for accurate optoacoustic endoscopy must be specified for optimal performance. Recent support from the EU Horizon 2020 program ESOTRAC to develop a next-generation optoacoustic esophageal endoscope directs the interrogation of the optimal frequency required for accurate implementation. We simulated the frequency response of the esophagus wall and then validated the simulation results with experimental measurements of pig esophagus. Phantoms and fresh pig esophagus samples were measured using two detectors with central frequencies of 15 or 50 MHz, and the imaging performance of both detectors was compared. We analyzed the frequency bandwidth of optoacoustic signals in relation to morphological layer structures of the esophagus and found the 50 MHz detector to differentiate layer structures better than the 15 MHz detector. Furthermore, we identify the necessary detection bandwidth for visualizing esophagus morphology and selecting ultrasound transducers for future optoacoustic endoscopy of the esophagus.

K. C. Santosh;Sameer Antani; "Automated Chest X-Ray Screening: Can Lung Region Symmetry Help Detect Pulmonary Abnormalities?," vol.37(5), pp.1168-1177, May 2018. Our primary motivator is the need for screening HIV+ populations in resource-constrained regions for exposure to Tuberculosis, using posteroanterior chest radiographs (CXRs). The proposed method is motivated by the observation that radiological examinations routinely conduct bilateral comparisons of the lung field. In addition, the abnormal CXRs tend to exhibit changes in the lung shape, size, and content (textures), and in overall, reflection symmetry between them. We analyze the lung region symmetry using multi-scale shape features, and edge plus texture features. Shape features exploit local and global representation of the lung regions, while edge and texture features take internal content, including spatial arrangements of the structures. For classification, we have performed voting-based combination of three different classifiers: Bayesian network, multilayer perception neural networks, and random forest. We have used three CXR benchmark collections made available by the U.S. National Library of Medicine and the National Institute of Tuberculosis and Respiratory Diseases, India, and have achieved a maximum abnormality detection accuracy (ACC) of 91.00% and area under the ROC curve (AUC) of 0.96. The proposed method outperforms the previously reported methods by more than 5% in ACC and 3% in AUC.

Md Tauhidul Islam;Anuj Chaudhry;Songyuan Tang;Ennio Tasciotti;Raffaella Righetti; "A New Method for Estimating the Effective Poisson’s Ratio in Ultrasound Poroelastography," vol.37(5), pp.1178-1191, May 2018. Ultrasound poroelastography aims at assessing the poroelastic behavior of biological tissues via estimation of the local temporal axial strains and effective Poisson's ratios (EPR). Currently, reliable estimation of EPR using ultrasound is a challenging task due to the limited quality of lateral strain estimation. In this paper, we propose a new two-step EPR estimation technique based on dynamic programming elastography (DPE) and Horn-Schunck (HS) optical flow estimation. In the proposed method, DPE is used to estimate the integer axial and lateral displacements while HS is used to obtain subsample axial and lateral displacements from the motion-compensated pre-compressed and post-compressed radio frequency data. Axial and lateral strains are then calculated using Kalman filter-based least square estimation. The proposed two-step technique was tested using finite-element simulations, controlled experiments and in vivo experiments, and its performance was statistically compared with that of analytic minimization (AM) and correlation-based method (CM). Our results indicate that our technique provides EPR elastograms of higher quality and accuracy than those produced by AM and CM. Regarding signal-to-noise ratio and elastographic contrast-to-noise ratio, in simulated data, the proposed method provides an average improvement of 30% and 75%, respectively, with respect to AM and of 100% and 169%, respectively, with respect to CM, whereas, in experiments, the proposed approach provides an average improvement of 30% and 67% with respect to AM and of 230% and 525% with respect to CM. Based on these results, the proposed method may be the preferred one in experimental poroelastography applications.

Marcel Straub;Volkmar Schulz; "Joint Reconstruction of Tracer Distribution and Background in Magnetic Particle Imaging," vol.37(5), pp.1192-1203, May 2018. Magnetic particle imaging (MPI) is a novel tomographic imaging technique, which visualizes the distribution of a magnetic nanoparticle-based tracer material. However, reconstructed MPI images often suffer from an insufficiently compensated image background caused by rapid non-deterministic changes in the background signal of the imaging device. In particular, the signal-to-background ratio (SBR) of the images is reduced for lower tracer concentrations or longer acquisitions. The state-of-the-art procedure in MPI is to frequently measure the background signal during the sample measurement. Unfortunately, this requires a removal of the entire object from the scanner's field of view (FOV), which introduces dead time and repositioning artifacts. To overcome these considerable restrictions, we propose a novel method that uses two consecutive image acquisitions as input parameters for a simultaneous reconstruction of the tracer distribution, as well as the background signal. The two acquisitions differ by just a small spatial shift, while keeping the object always within the focus of a slightly reduced FOV. A linearly interpolated background between the initial and final background measurement is used to seed the iterative reconstruction. The method has been tested with simulations and phantom measurements. Overall, a substantial reduction of the image background was observed, and the image SBR is increased by a factor of 2(7) for the measurement (simulation) data.

M. Allan;S. Ourselin;D. J. Hawkes;J. D. Kelly;D. Stoyanov; "3-D Pose Estimation of Articulated Instruments in Robotic Minimally Invasive Surgery," vol.37(5), pp.1204-1213, May 2018. Estimating the 3-D pose of instruments is an important part of robotic minimally invasive surgery for automation of basic procedures as well as providing safety features, such as virtual fixtures. Image-based methods of 3-D pose estimation provide a non-invasive low cost solution compared with methods that incorporate external tracking systems. In this paper, we extend our recent work in estimating rigid 3-D pose with silhouette and optical flow-based features to incorporate the articulated degrees-of-freedom (DOFs) of robotic instruments within a gradient-based optimization framework. Validation of the technique is provided with a calibrated ex-vivo study from the da Vinci Research Kit (DVRK) robotic system, where we perform quantitative analysis on the errors each DOF of our tracker. Additionally, we perform several detailed comparisons with recently published techniques that combine visual methods with kinematic data acquired from the joint encoders. Our experiments demonstrate that our method is competitively accurate while relying solely on image data.

Daniel C. Mellema;Pengfei Song;Randall R. Kinnick;Joshua D. Trzasko;Matthew W. Urban;James F. Greenleaf;Armando Manduca;Shigao Chen; "Probe Oscillation Shear Wave Elastography: Initial In Vivo Results in Liver," vol.37(5), pp.1214-1223, May 2018. Shear wave elastography methods are able to accurately measure tissue stiffness, allowing these techniques to monitor the progression of hepatic fibrosis. While many methods rely on acoustic radiation force to generate shear waves for 2-D imaging, probe oscillation shear wave elastography (PROSE) provides an alternative approach by generating shear waves through continuous vibration of the ultrasound probe while simultaneously detecting the resulting motion. The generated shear wave field in in vivo liver is complicated, and the amplitude and quality of these shear waves can be influenced by the placement of the vibrating probe. To address these challenges, a real-time shear wave visualization tool was implemented to provide instantaneous visual feedback to optimize probe placement. Even with the real-time display, it was not possible to fully suppress residual motion with established filtering methods. To solve this problem, the shear wave signal in each frame was decoupled from motion and other sources through the use of a parameter-free empirical mode decomposition before calculating shear wave speeds. This method was evaluated in a phantom as well as in in vivo livers from five volunteers. PROSE results in the phantom as well as in vivo liver correlated well with independent measurements using the commercial General Electric Logiq E9 scanner.

Biao Cai;Pascal Zille;Julia M. Stephen;Tony W. Wilson;Vince D. Calhoun;Yu Ping Wang; "Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI," vol.37(5), pp.1224-1234, May 2018. Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret. In addition, their estimations to a large extent were based on an implicit assumption of spatial and temporal stationarity throughout the fMRI scanning session. In this paper, we propose an approach called dynamic sparse connectivity patterns (dSCPs), which takes advantage of both matrix factorization and time-varying fMRI time series to improve the estimation power of FC. The feasibility of analyzing dynamic FC with our model is first validated through simulated experiments. Then, we use our framework to measure the difference between young adults and children with real fMRI data set from the Philadelphia Neurodevelopmental Cohort (PNC). The results from the PNC data set showed significant FC differences between young adults and children in four different states. For instance, young adults had reduced connectivity between the default mode network and other subnetworks, as well as hyperconnectivity within the visual system in states 1 and 3, and hypoconnectivity in state 2. Meanwhile, they exhibited temporal correlation patterns that changed over time within functional subnetworks. In addition, the dSCPs model indicated that older people tend to spend more time within a relatively connected FC pattern. Overall, the proposed method provides a valid means to assess dynamic FC, which could facilitate the study of brain networks.

Denis Fortun;Paul Guichard;Virginie Hamel;Carlos Oscar S. Sorzano;Niccolò Banterle;Pierre Gönczy;Michael Unser; "Reconstruction From Multiple Particles for 3D Isotropic Resolution in Fluorescence Microscopy," vol.37(5), pp.1235-1246, May 2018. The imaging of proteins within macromolecular complexes has been limited by the low axial resolution of optical microscopes. To overcome this problem, we propose a novel computational reconstruction method that yields isotropic resolution in fluorescence imaging. The guiding principle is to reconstruct a single volume from the observations of multiple rotated particles. Our new operational framework detects particles, estimates their orientation, and reconstructs the final volume. The main challenge comes from the absence of initial template and a priori knowledge about the orientations. We formulate the estimation as a blind inverse problem, and propose a block-coordinate stochastic approach to solve the associated non-convex optimization problem. The reconstruction is performed jointly in multiple channels. We demonstrate that our method is able to reconstruct volumes with 3D isotropic resolution on simulated data. We also perform isotropic reconstructions from real experimental data of doubly labeled purified human centrioles. Our approach revealed the precise localization of the centriolar protein Cep63 around the centriole microtubule barrel. Overall, our method offers new perspectives for applications in biology that require the isotropic mapping of proteins within macromolecular assemblies.

Limin Zhang;Shudong Jiang;Yan Zhao;Jinchao Feng;Brian W. Pogue;Keith D. Paulsen; "Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions," vol.37(5), pp.1247-1252, May 2018. An approach using direct regularization from co-registered dynamic contrast enhanced magnetic reson- ance images was used to reconstruct near-infrared spectral tomography patient images, which does not need image segmentation. 20 patients with mammography/ultrasound confirmed breast abnormalities were involved in this paper, and the resulting images indicated that tumor total hemoglobin concentration contrast differentiated malignant from benign cases (p-value = 0.021). The approach prod- uced reconstructed images, which significantly reduced surface artifacts near the source-detector locations (p-value = 4.16e-6).

Melissa W. Haskell;Stephen F. Cauley;Lawrence L. Wald; "TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization," vol.37(5), pp.1253-1265, May 2018. We introduce a data consistency based retrospective motion correction method, TArgeted Motion Estimation and Reduction (TAMER), to correct for patient motion in Magnetic Resonance Imaging (MRI). Specifically, a motion free image and motion trajectory are jointly estimated by minimizing the data consistency error of a SENSE forward model including rigid-body subject motion. In order to efficiently solve this large non-linear optimization problem, we employ reduced modeling in the parallel imaging formulation by assessing only a subset of target voxels at each step of the motion search. With this strategy we are able to effectively capture the tight coupling between the image voxel values and motion parameters. We demonstrate in simulations TAMER's ability to find similar search directions compared to a full model, with an average error of 22%, vs. 73% error when using previously proposed alternating methods. The reduced model decreased the computation time 17× fold compared to a full image volume evaluation. In phantom experiments, our method successfully mitigates both translation and rotation artifacts, reducing image RMSE compared to a motion-free gold standard from 21% to 14% in a translating phantom, and from 17% to 10% in a rotating phantom. Qualitative image improvements are seen in human imaging of moving subjects compared to conventional reconstruction. Finally, we compare in vivo image results of our method to the state-of-the-art.

Haofu Liao;Addisu Mesfin;Jiebo Luo; "Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information," vol.37(5), pp.1266-1275, May 2018. Automatic vertebrae identification and localization from arbitrary computed tomography (CT) images is challenging. Vertebrae usually share similar morphological appearance. Because of pathology and the arbitrary field-of-view of CT scans, one can hardly rely on the existence of some anchor vertebrae or parametric methods to model the appearance and shape. To solve the problem, we argue that: 1) one should make use of the short-range contextual information, such as the presence of some nearby organs (if any), to roughly estimate the target vertebrae; and 2) due to the unique anatomic structure of the spine column, vertebrae have fixed sequential order, which provides the important long-range contextual information to further calibrate the results. We propose a robust and efficient vertebrae identification and localization system that can inherently learn to incorporate both the short- and long-range contextual information in a supervised manner. To this end, we develop a multi-task 3-D fully convolutional neural network to effectively extract the short-range contextual information around the target vertebrae. For the long-range contextual information, we propose a multi-task bidirectional recurrent neural network to encode the spatial and contextual information among the vertebrae of the visible spine column. We demonstrate the effectiveness of the proposed approach on a challenging data set, and the experimental results show that our approach outperforms the state-of-the-art methods by a significant margin.

Xiaofei Du;Thomas Kurmann;Ping-Lin Chang;Maximilian Allan;Sebastien Ourselin;Raphael Sznitman;John D. Kelly;Danail Stoyanov; "Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks," vol.37(5), pp.1276-1287, May 2018. Instrument detection, pose estimation, and tracking in surgical videos are an important vision component for computer-assisted interventions. While significant advances have been made in recent years, articulation detection is still a major challenge. In this paper, we propose a deep neural network for articulated multi-instrument 2-D pose estimation, which is trained on detailed annotations of endoscopic and microscopic data sets. Our model is formed by a fully convolutional detection-regression network. Joints and associations between joint pairs in our instrument model are located by the detection subnetwork and are subsequently refined through a regression subnetwork. Based on the output from the model, the poses of the instruments are inferred using maximum bipartite graph matching. Our estimation framework is powered by deep learning techniques without any direct kinematic information from a robot. Our framework is tested on single-instrument RMIT data, and also on multi-instrument EndoVis and in vivo data with promising results. In addition, the data set annotations are publicly released along with our code and model.

* "IEEE Life Sciences Conference," vol.37(5), pp.1288-1288, May 2018.* Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

* "IEEE Transactions on Medical Imaging information for authors," vol.37(5), pp.C3-C3, May 2018.* These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

IET Image Processing - new TOC (2018 May 24) [Website]

Michela Lecca;Gabriele Simone;Cristian Bonanomi;Alessandro Rizzi; "Point-based spatial colour sampling in Milano-Retinex: a survey," vol.12(6), pp.833-849, 6 2018. Milano-Retinex is a family of Retinex-inspired spatial colour algorithms mainly developed for colour image enhancement. According to the Retinex theory, a Milano-Retinex algorithm takes as input an RGB image and processes the colour intensities of each pixel (i.e. the target) based on the spatial distribution of the colour intensities sampled in a surrounding region. The output is an RGB image, with locally adjusted colours and contrast. In Milano-Retinex family, different ways of spatial sampling are implemented. This study reviews and compares these sampling characteristics within a group of Milano-Retinex algorithms developed in the last decade, from Random Spray Retinex (2007) to the gradient-based colour sampling schemes GREAT and GRASS (2017). Instead of exploring the target neighbourhood by random paths as the original Retinex algorithm does, these methods consider sets of pixels, randomly or deterministically defined, including all the image pixels or a part of them, such as random sprays or image edges. They replace the ratio-reset-threshold-product-average mechanism of the original Retinex with equations re-working maximal intensities over the sampled sets. The performance of these approaches is compared with more than 200 images of indoor and outdoor scenes, captured by commercial cameras under several different conditions.

Yuena Ma;Xiaoyi Feng;Yang Liu;Shuhong Li; "BCH–LSH: a new scheme of locality-sensitive hashing," vol.12(6), pp.850-855, 6 2018. Similarity searching of high-dimensional data is fundamental in the multimedia research field. In recent years, the binary code indexing has achieved significant applications in the context of similarity searching. However, most of the existing binary coding methods adopt a random generation method in near neighbour cluster problems, which involve unnecessary computations and degrade similarity in object points. To avoid the uncertainty of random generation codes, in this study, the authors propose a new locality sensitive hashing (LSH) algorithm based on q-ary Bose–Chaudhuri–Hocquenghem (BCH) code. BCH–LSH algorithm utilises the characteristics of the designed distance of BCH codes and uses the BCH codes generator matrix as a transform basis of the hash function to map the source data into the hash space. The experiments show that the BCH–LSH algorithm is superior to the E2LSH algorithm in average precision, average recall ratio and running speed.

Bin Gou;Yong-mei Cheng; "Automatic centroid extraction method for noisy star image," vol.12(6), pp.856-862, 6 2018. Star images obtained by star sensors have a low signal-to-noise ratio due to various physical constraints. Low resolution also causes stellar centroid extraction error when traditional methods such as the Gaussian filter or adaptive median filter are utilised to de-noise star images. An automatic centroid extraction method for noisy low-resolution star images is proposed in this study. First, sparse representation is utilised to de-noise the Poisson–Gaussian mixed noise of the low-resolution star image. A high-resolution star image is then reconstructed by using the low-resolution sparse coefficient. Finally, the stellar centroids are extracted automatically by learning the relationship between the high-resolution star image and corresponding stellar centroid image. Experimental results indicate that the positioning accuracy of the stellar centroids is also greatly enhanced by the reconstructed high-resolution stellar centroid image. The correct rate of stellar centroid recognition is 99.35%; the positioning accuracy of stellar centroid and computing time are 16.21″ and 11.30 ms, respectively. The probability distributions of Poisson and Gaussian noises are 0.50 and 0.08, respectively, while the proposed method correctly recognises stellar centroids at a rate of 76.56%. The results presented here may provide a workable foundation for accurate attitude calculations of the celestial navigation system.

Jiayi Chen;Yinwei Zhan;Huiying Cao;Xingda Wu; "Adaptive probability filter for removing salt and pepper noises," vol.12(6), pp.863-871, 6 2018. To overcome the drawbacks of existing filters for salt and pepper noises, an adaptive probability filter is proposed. For an image, it detects salt and pepper noises based on the characteristic of minimum and maximum intensity values of the images, as well as the distribution of noise. If the noise-free intensities in neighbourhood repeat with a certain probability, the noise-free intensity with highest repeated frequency is used to remove noise based on the statistical significance; otherwise, the median of noise-free pixels in neighbourhood is used to remove noise. Experiments show that the proposed method is capable of detecting noise more accurately and perform much better than the existing distinguished filters in terms of peak-signal-to-noise ratio, image enhancement factor, and visual representation at all the noise densities.

Juncai Yao;Guizhong Liu; "Improved SSIM IQA of contrast distortion based on the contrast sensitivity characteristics of HVS," vol.12(6), pp.872-879, 6 2018. Currently, the structural similarity index metric (SSIM) is recognised generally and applied widely in image quality assessment (IQA). However, using SSIM to evaluate contrast-distorted images from TID2013 and CSIQ databases is low effective. In this study, the authors improve SSIM for contrast-distorted images by combining it with the contrast sensitivity characteristics of human visual system (HVS). In the improved method, first, they combine the visual characteristics to propose a model that HVS perceives the real image. Then, this model is used to eliminate the visual redundancy of real images. Afterwards, the perceived images are evaluated using SSIM. Furthermore, 241 contrast-distorted images from TID2013 and CSIQ databases were used in experiments. The results have shown that in comparison with SSIM scores, the scores obtained by the improved SSIM are more consistent with the subjective assessment scores. Moreover, the Pearson linear correlation coefficient and Spearman rank order correlation coefficient between subjective and objective scores are averagely improved by 12.83 and 22.78%, respectively. In addition, the assessment accuracy of the improved SSIM is better than that of five commonly used IQA metrics. Also, it has an excellent generalisation performance. These results show that the assessment performance of the improved SSIM is effectively enhanced.

Yuhong Liu;Hongmei Yan;Shaobing Gao;Kaifu Yang; "Criteria to evaluate the fidelity of image enhancement by MSRCR," vol.12(6), pp.880-887, 6 2018. Image fidelity refers to the ability of a process to render an image accurately. As image enhancement algorithms have been developed in recent years, how to assess the performances of different image enhancement algorithms has become an important question. Some objective image quality assessment (IQA) methods have been proposed, but there is little research on the image fidelity evaluation when comparing the performances of enhancement algorithms. Therefore, the authors proposed a new image fidelity assessment framework consisting of three components: the information entropy fidelity, constituent fidelity and colour fidelity. To verify the rationality of the fidelity criteria, they used the popular IQA database (LIVE), and the results indicated that the method matched better with the subjective assessment. Then, they verified the effectiveness of their method with the famous technique of image enhancement: multi-scale Retinex with colour restoration (MSRCR). The experimental results demonstrate MSRCR can improve the image quality, but it gives rise to obvious distortions. It is necessary to keep a moderate balance between image fidelity and image quality when they assess the enhanced images. Their results showed that the proposed objective fidelity index could provide an additional objective basis for the quality evaluation of image enhancement algorithms.

Huanjie Tao;Xiaobo Lu; "Correction of micro-CT image geometric artefacts based on marker," vol.12(6), pp.888-895, 6 2018. Small geometric misalignments of micro computed tomography (CT) system will cause geometric artefacts in the reconstructed image. A new correction method of geometrical artefacts based on marker and non-linear optimisation model is proposed. In this method, the simple balls marker and the measured objects are scanned simultaneously, and the geometric parameters of the micro-CT system are precisely estimated by solving the non-linear optimisation model which is based on the scanning data. The geometric artefacts caused by geometric parameters are corrected and the authors can reconstruct the image correctly. In addition to estimating geometric parameters for the traditional scanning mode, the proposed method can also be used for the limited angle CT scanning and the half detector CT scanning. Simulated experiments and real experiments verify that the correction method effectively decrease the geometric artefacts of micro-CT images.

Ibrahim El-Henawy;Kareem Ahmed;Hamdi Mahmoud; "Action recognition using fast HOG3D of integral videos and Smith–Waterman partial matching," vol.12(6), pp.896-908, 6 2018. Recognising human activity from video stream has become one of the most interesting applications in computer vision. In this study, a novel hybrid technique for human action recognition is proposed based on fast HOG3D of integral videos and Smith–Waterman partial shape matching of the fused frame. The proposed technique is divided into two main stages, the first stage extracts a set of foreground snippets from the input video, and extracts the histogram of 3D gradient orientations from the spatio-temporal volumetric data; and the second stage fuses a set of key frames from current snippet and extracts the contours from the fused frame. Non-linear support vector machine (SVM) decision trees are used to classify HOG3D features into one of fixed action categories. On the other hand, Smith–Waterman partial shape matching is used to compare between the contour of the fused frame and the stored template contour of specified action. The results from SVM and Smith–Waterman partial shape matching are then combined. The experimental results show that combining non-linear SVM decision trees of HOG3D features and Smith–Waterman partial shape matching of fused contours improved the accuracy of the classification model while maintaining efficiency in time elapsed for training.

Susant Kumar Panigrahi;Supratim Gupta;Prasanna K. Sahu; "Curvelet-based multiscale denoising using non-local means & guided image filter," vol.12(6), pp.909-918, 6 2018. This study presents an image denoising technique using multiscale non-local means (NLM) filtering combined with hard thresholding in curvelet domain. The inevitable ringing artefacts in the reconstructed image – due to thresholding – is further processed using a guided image filter for better preservation of local structures like edges, textures and small details. The authors decomposed the image into three different curvelet scales including the approximation and the fine scale. The low-frequency noise in the approximation sub-band and the edges with small textural details in the fine scale are processed independently using a multiscale NLM filter. On the other hand, the hard thresholding in the remaining coarser scale is applied to separate the signal and the noise subspace. Experimental results on both greyscale and colour images indicate that the proposed approach is competitive at lower noise strength with respect to peak signal to noise ratio and structural similarity index measure and excels in performance at higher noise strength compared with several state-of-the-art algorithms.

Sultan Mohammad Mohaimin;Sajib Kumar Saha;Alve Mahamud Khan;Abu Shamim Mohammad Arif;Yogesan Kanagasingam; "Automated method for the detection and segmentation of drusen in colour fundus image for the diagnosis of age-related macular degeneration," vol.12(6), pp.919-927, 6 2018. Age-related macular degeneration (AMD) is one of the main reasons for visual impairment worldwide. The assessment of risk for the development of AMD requires reliable detection and quantitative mapping of retinal abnormalities that are considered as precursors of the disease. Typical signs of the latter are the so-called drusen that appear as yellowish spots in the retina. Automated detection and segmentation of drusen provide vital information about the severity of the disease. The authors propose a novel method for the detection and segmentation of drusen in colour fundus images. The method combines colour information of the object with its boundary information for the accurate detection and segmentation of drusen. To perform non-uniform illumination correction and to minimise inter-subject variability a novel colour normalisation method has been proposed. Experiments are conducted on publicly available STARE and ARIA datasets. The method achieves an overall accuracy of 96.62% which is about 4% higher than the state-of-the-art method. The sensitivity and specificity of the proposed method are 95.96 and 97.64%, respectively.

Amine Laghrib;Mohamed Alahyane;Abdelghani Ghazdali;Abdelilah Hakim;Said Raghay; "Multiframe super-resolution based on a high-order spatially weighted regularisation," vol.12(6), pp.928-940, 6 2018. Here, the authors propose a spatially weighted super-resolution (SR) algorithm, which takes into consideration the distribution of every information that characterise different image areas. The authors investigate to use a combined spatially weighted regularisation of the bilateral total variation and a second-order term increasing then the robustness of the proposed SR approach with respect to blur and noise degradations. In addition, the authors propose an iterative Bregman iteration algorithm to resolve the obtained optimisation SR problem. As a result, this regularisation is more efficient and easier to implement; moreover, it preserves well the smooth regions of the image and also sharp edges. Using different simulated and real tests, the authors prove the efficiency of the proposed algorithm compared to some SR methods.

Haimiao Ge;Liguo Wang;Yanzhong Liu;Cheng Li;Ruixin Chen; "Hyperspectral image classification based on adaptive-weighted LLE and clustering-based FSVMs," vol.12(6), pp.941-947, 6 2018. An improved version of supervised locally linear embedding is proposed. In this algorithm, the weight factors of the supervised method are adaptively achieved. This method can simplify the supervised feature extraction algorithm by reducing parameters. To improve classification accuracy, a clustering-based fuzzy support vector machine (FSVM) is proposed. Different from traditional FSVMs, the proposed method constructs the fuzzy weights by inner-class clusters. In the proposed method, loose density is defined to express the compactness of the inner-class clusters. The proposed algorithm can restrain the noise and outliers by exploiting the method of endowing with smaller weight for big loose density and bigger weight for the small loose density of samples in the clusters. To inspect the performance of the proposed methods, we conduct experiments on two hyper-spectral images. Results show that the two methods are competitive among the competitors.

Mahdi Dodangeh;Isabel N. Figueiredo;Gil Gonçalves; "Spatially adaptive total variation deblurring with split Bregman technique," vol.12(6), pp.948-958, 6 2018. In this study, the authors describe a modified non-blind and blind deconvolution model by introducing a regularisation parameter that incorporates the spatial image information. Indeed, they have used a weighted total variation term, where the weight is a spatially adaptive parameter based on the image gradient. The proposed models are solved by the split Bregman method. To handle adequately the discrete convolution transform in a moderate time, fast Fourier transform is used. Tests are conducted on several images, and for assessing the results, they define appropriate weighted versions of two standard image quality metrics. These new weighted metrics clearly highlight the advantage of the spatially adaptive approach.

Fanlong Zhang;Zhangjing Yang;Yu Chen;Jian Yang;Guowei Yang; "Matrix completion via capped nuclear norm," vol.12(6), pp.959-966, 6 2018. Matrix completion is to recover a low-rank matrix from a subset of its entries. One of the solution strategies is based on nuclear norm minimisation. However, since the nuclear norm is defined as the sum of all singular values, each of which is treated equally, the rank function may not be well approximated in practice. To overcome this drawback, this study presents a matrix completion method based on capped nuclear norm (MC-CNN). The capped nuclear norm can reflect the rank function more directly and accurately than the nuclear norm, Schatten p-norm (to the power p) and truncated nuclear norm. The relation between the capped nuclear norm and the truncated nuclear norm is revealed for the first time. Difference of convex functions’ programming is employed to solve MC-CNN. In the proposed algorithm, a key sub-problem, i.e. a matrix completion problem with linear regularisation term, is solved by using the active subspace selection method. In addition, the algorithm convergence is discussed. Experimental results show encouraging results of the proposed algorithm in comparison with the state-of-the-art matrix completion methods on both synthetic and real visual datasets.

Walid El-Shafai;El-Sayed M. El-Rabaie;Mohamed M. El-Halawany;Fathi E. Abd El-Samie; "Proposed adaptive joint error-resilience concealment algorithms for efficient colour-plus-depth 3D video transmission," vol.12(6), pp.967-984, 6 2018. The authors propose efficient hybrid error resilience and error concealment (ER-EC) algorithms for H.264 3D video-plus-depth (3DV + D) transmission over error-prone channels. At the encoder, content-adaptive pre-processing ER mechanisms are implemented by applying the context adaptive variable length coding (CAVLC), the slice structured coding, and the explicit flexible macro-block ordering. At the decoder, a post-processing EC algorithm with multi-proposition schemes is implemented to recover the lost 3DV colour frames. The convenient EC hypothesis is adopted based on the lost macro-blocks size mode, the faulty view, and the frame types. For the recovery of the lost 3DV depth frames, an encoder-independent decoder-dependent depth-assisted EC algorithm is suggested. It exploits the previously estimated colour disparity vectors (DVs) and motion vectors (MVs) to estimate more additional depth-assisted MVs and DVs. After that, the optimum colour-plus-depth DVs and MVs are accurately selected by employing the directional interpolation EC algorithm and the decoder MV estimation algorithm. Finally, a weighted overlapping block motion and disparity compensation scheme is utilised to reinforce the performance of the proposed hybrid ER-EC algorithms. Experimental results on standard 3DV + D sequences show that the proposed hybrid algorithms have superior objective and subjective performance indices.

Lijian Zhou;Chen Zhang;Zuowei Wang;Ying Wang;Zhe-Ming Lu; "Hierarchical palmprint feature extraction and recognition based on multi-wavelets and complex network," vol.12(6), pp.985-992, 6 2018. This study presents a hierarchical palmprint feature extraction and recognition approach based on multi-wavelet and complex network (CN) since they can effectively decrease redundant information and enhance key points of main lines and wrinkles. The palmprint is first pre-filtered and decomposed once using multi-wavelet. Three components (LL1,2,3) corresponding to the pre-filter except for diagonal component are extracted as the elementary features. Second, binary images (BLL1,2,3) are obtained by the average window method using different thresholds. Third, three series of dynamic evolution CN models (the 1st, 2nd, 3rd CNs) are constructed from global to local, which is based on the mosaiced images obtained from BLL1,2,3, BLL1 and four equally divided sub-images of BLL1, respectively. Fourth, statistical features are extracted from complex networks, in which average degree and standard deviation of the degrees are extracted for the 1st CNs and average degrees are extracted for the 2nd and 3rd CNs. Fifth, the fisher feature is extracted using the linear discriminate analysis method. Finally, the nearest neighbourhood classifier is used to recognise palmprint. Based on the CASIA Palmprint Image Database, experimental results show that the proposed method can effectively recognise palmprint with good robustness and overcome the problem of small training samples number.

Chuan Lin;Guili Xu;Yijun Cao; "Contour detection model using linear and non-linear modulation based on non-CRF suppression," vol.12(6), pp.993-1003, 6 2018. Psychophysical and neurophysiological investigations on the human visual system show that most neurons in the primary visual cortex (V1) possess a non-classical receptive field (nCRF) region in addition to the CRF region. The nCRF has a modulatory, normally inhibitory, effect on the responses to visual stimuli generated within the CRF. In computational terms, this mechanism suppresses the response to edges in the presence of similar edges in the surroundings. Many computational techniques have been proposed to address the surround suppression mechanism. These methods introduce an inhibition term that is required to suppress the textures and protect the contours. Several studies have found that the spatial summation properties over the receptive fields of retinal X cells are approximately linear, while they are non-linear for Y cells. Inspired by the visual information processing in the X–Y channel and spatial summation properties of X and Y cells, the authors propose a contour detector using linear and non-linear modulations based on nCRF suppression. Extensive experimental evaluations demonstrate that their contour detector significantly outperforms other algorithms. The methods proposed in this study are expected to facilitate the development of efficient computational models in the field of machine vision.

Tehmina Kalsum;Syed Muhammad Anwar;Muhammad Majid;Bilal Khan;Sahibzada Muhammad Ali; "Emotion recognition from facial expressions using hybrid feature descriptors," vol.12(6), pp.1004-1012, 6 2018. Here, a hybrid feature descriptor-based method is proposed to recognise human emotions from their facial expressions. A combination of spatial bag of features (SBoFs) with spatial scale-invariant feature transform (SBoF-SSIFT), and SBoFs with spatial speeded up robust transform are utilised to improve the ability to recognise facial expressions. For classification of emotions, K-nearest neighbour and support vector machines (SVMs) with linear, polynomial, and radial basis function kernels are applied. SBoFs descriptor generates a fixed length feature vector for all sample images irrespective of their size. Spatial SIFT and SURF features are independent of scaling, rotation, translation, projective transforms, and partly to illumination changes. A modified form of bag of features (BoFs) is employed by involving feature's spatial information for facial emotion recognition. The proposed method differs from conventional methods that are used for simple object categorisation without using spatial information. Experiments have been performed on extended Cohn–Kanade (CK+) and Japanese female facial expression (JAFFE) data sets. SBoF-SSIFT with SVM resulted in a recognition accuracy of 98.5% on CK+ and 98.3% on JAFFE data set. Images are resized through selective pre-processing, thereby retaining only the information of interest and reducing computation time.

Nidhi Saxena;Kamalesh K. Sharma; "Pansharpening scheme using filtering in two-dimensional discrete fractional Fourier transform," vol.12(6), pp.1013-1019, 6 2018. The aim of the pansharpening scheme is to improve the spatial information of multispectral images using the panchromatic (PAN) image. In this study, a novel pansharpening scheme based on two-dimensional discrete fractional Fourier transform (2D-DFRFT) is proposed. In the proposed scheme, PAN and intensity images are transformed using 2D-DFRFT and filtered by highpass filters, respectively. The filtered images are inverse transformed and further used to generate the pansharpened image using appropriate fusion rule. The additional degree of freedom in terms of its angle parameters associated with the 2D-DFRFT is exploited for obtaining better results in the proposed pansharpening scheme. Simulation results of the proposed technique carried out in MATLAB are presented for IKONOS and GeoEye-1 satellite images and compared with existing fusion methods in terms of both visual observation and quality metrics. It is seen that the proposed pansharpening scheme has improved spectral and spatial resolution as compared to the existing schemes.

Lamjed Touil;Ismail Gassoumi;Radhouane Laajimi;Bouraoui Ouni; "Efficient design of BinDCT in quantum-dot cellular automata (QCA) technology," vol.12(6), pp.1020-1030, 6 2018. Here, the authors present a hardware design of fast multiplierless forward binary discrete cosine transform (BinDCT) based on quantum-dot cellular automata (QCA) technology. This new technology offers several features such as: small size, ultralow power consumption, and can operate at 1 THz. The simulation results in QCA Designer software confirm that the proposed circuit works well and can be used as a high-performance design in QCA technology. The analysis obtained from the implementation of QCA BinDCT indicates that the proposed architecture is superior to the existing based on classic metal-oxide (complementary metal-oxide semiconductor technology) technology. Here, the authors are going to introduce highly BinDCT module scaled with ultra-low power consuming. The proposed circuit requires 50% fewer power consuming compared to previous existing designs. The proposed architecture can attain a throughput of 800 mega pixel per second (Mpps). To design and verify the proposed architecture, QCADesigner tool and QCAPro tool are, respectively, employed for synthesis and power consumption estimation. Since the works in the field of QCA logic image processing have only started to bloom, the proposed contribution will engender a new thread of research in the field of real-time image and video treatment.

Sheng Long Lee;Mohammad Reza Zare; "Biomedical compound figure detection using deep learning and fusion techniques," vol.12(6), pp.1031-1037, 6 2018. Images contain significant amounts of information but present different challenges relative to textual information. One such challenge is compound figures or images made up of two or more subfigures. A deep learning model is proposed for compound figure detection (CFD) in the biomedical article domain. First, pre-trained convolutional neural networks (CNNs) are selected for transfer learning to take advantage of the image classification performance of CNNs and to overcome the limited dataset of the CFD problem. Next, the pre-trained CNNs are fine-tuned on the training data with early-stopping to avoid overfitting. Alternatively, layer activations of the pre-trained CNNs are extracted and used as input features to a support vector machine classifier. Finally, individual model outputs are combined with score-based fusion. The proposed combined model obtained a best test accuracy of 90.03 and 96.93% outperforming traditional hand-crafted and other deep learning representations on the ImageCLEF 2015 and 2016 CFD subtask datasets, respectively, by using AlexNet, VGG-16, VGG-19 pre-trained CNNs fine-tuned until best validation accuracy stops increasing combined with the combPROD score-based fusion operator.

Sukhvir Kaur;Shreelekha Pandey;Shivani Goel; "Semi-automatic leaf disease detection and classification system for soybean culture," vol.12(6), pp.1038-1048, 6 2018. Development of automatic disease detection and classification system is significantly explored in precision agriculture. In the past few decades, researchers have studied several cultures exploiting different parts of a plant. A similar study is performed for Soybean using leaf images. A rule based semi-automatic system using concepts of k-means is designed and implemented to distinguish healthy leaves from diseased leaves. In addition, a diseased leaf is classified into one of the three categories (downy mildew, frog eye, and Septoria leaf blight). Experiments are performed by separately utilising colour features, texture features, and their combinations to train three models based on support vector machine classifier. Results are generated using thousands of images collected from PlantVillage dataset. Acceptable average accuracy values are reported for all the considered combinations which are also found to be better than existing ones. This study also attempts to discover the best performing feature set for leaf disease detection in Soybean. The system is shown to efficiently compute the disease severity as well. Visual examination of leaf samples further proves the suitability of the proposed system for detection, classification, and severity calculation.

Libao Zhang;Shiyi Wang;Xiaohan Wang; "Saliency-based dark channel prior model for single image haze removal," vol.12(6), pp.1049-1055, 6 2018. Images degraded by haze usually have low contrast and fide colours, and thus have bad effects on applications such as object tracking, face recognition, and intelligent surveillance. So the purpose of dehazing is to recover the image contrast without colour distortion. The dark channel prior (DCP) is widely used in the field of haze removal because of its simplicity and effectiveness. However, when faced with bright white objects, DCP overestimates the haze from its true value and thus causes colour distortion. In this study, the authors propose a dehazing model combining saliency detection with DCP to obtain recovered images with little colour distortion. There are three main contributions. First, they introduce a novel saliency detection method, focusing on superpixel intensity contrast, to extract bright white objects in the hazy image. Those objects are not used to estimate the atmospheric light and transmission in the dark channel image. Second, a self-adaptive upper bound is set for the scene radiance to prevent some regions being too bright. Third, they propose a quantitative indicator, colour variance distance, to evaluate the colour restoration. Experimental results show that their proposed model generates less colour distortion and has better comprehensive performance than competing models.

Tauheed Ahmed;Monalisa Sarma; "Locality sensitive hashing based space partitioning approach for indexing multidimensional feature vectors of fingerprint image data," vol.12(6), pp.1056-1064, 6 2018. In recent years, biometric applications have significantly gained popularity. Such applications involve voluminous databases of high dimensional data. These enormous databases increase the cost of identification and degrade the system performance. To resolve such an issue a plethora of algorithms based on geometric hashing, k–d tree, k-means clustering, etc., have been proposed in the literature. Although, these algorithms solve a number of concomitant challenges of multi-dimensional data, yet, they fail to present a universal solution. In this study, we propose an indexing mechanism, which partitions the data space effectively into zones and blocks using a set of hash functions. Furthermore, the index locations are divided into maximum nine sub-locations to store data. This helps in carrying out an efficient search of the queried data, thereby minimising the false acceptance and rejection rate. To validate the proposed approach, the mechanism has been applied to the fingerprint verification competition and National Institute of Standards and Technology fingerprint image databases. The experimental results substantiate the efficacy of our approach in terms of accuracy, speed, reduction of search space and the number of comparisons to store and retrieve data.

Vijay Kumar Sharma;Devesh Kumar Srivastava;Pratistha Mathur; "Efficient image steganography using graph signal processing," vol.12(6), pp.1065-1071, 6 2018. Steganography is used for secret or covert communication. A graph wavelet transform-based steganography using graph signal processing (GSP) is presented, which results in better visual quality stego image as well as extracted secret image. In the proposed scheme, graph wavelet transforms of both the cover image and transformed secret image (using Arnold cat map) are taken followed by alpha blending operation. The GSP-based inverse wavelet transform is performed on the resulting image, to get the stego image. Here, the use of GSP increases the inter-pixel correlation that results in better visual quality stego and extracted secret image as shown in simulation results. Simulation results show that the proposed scheme is more robust than other existing steganography techniques.

IEEE Transactions on Signal Processing - new TOC (2018 May 24) [Website]

Mianzhi Wang;Zhen Zhang;Arye Nehorai; "Performance Analysis of Coarray-Based MUSIC in the Presence of Sensor Location Errors," vol.66(12), pp.3074-3085, June15, 15 2018. Sparse linear arrays, such as co-prime and nested arrays, can resolve more uncorrelated sources than the number of sensors by applying the MUtiple SIgnal Classification (MUSIC) algorithm to their difference coarray model. We aim at statistically analyzing the performance of the MUSIC algorithm applied to the difference coarray model, namely, the coarray-based MUSIC, in the presence of sensor location errors. We first introduce a signal model for sparse linear arrays in the presence of deterministic unknown location errors. Based on this signal model, we derive a closed-form expression of the asymptotic mean-squared error of a commonly used coarray-based MUSIC algorithm, SS-MUSIC, in the presence of small sensor location errors. We show that the sensor location errors introduce a constant bias that depends on both the physical array geometry and the coarray geometry, which cannot be mitigated by only increasing the signal-to-noise ratio. We next give a short extension of our analysis to cases when the sensor location errors are stochastic and investigate the Gaussian case. Finally, we derive the Cramér–Rao bound for joint estimation of direction-of-arrivals and sensor location errors for sparse linear arrays, which can be applicable even if the number of sources exceeds the number of sensors. Numerical simulations show good agreement between empirical results and our theoretical results.

Zhenqian Wang;Yongqiang Wang; "Pulse-Coupled Oscillators Resilient to Stealthy Attacks," vol.66(12), pp.3086-3099, June15, 1 2018. Synchronization of bio-inspired pulse-coupled oscillators is receiving increased attention due to its wide applications in sensor networks and wireless communications. However, most existing results are obtained in the absence of malicious attacks. Given the distributed and unattended nature of wireless sensor networks, it is imperative to enhance the resilience of pulse based synchronization against malicious attacks. To achieve this goal, we propose a new pulse based interaction mechanism to improve the resilience of pulse based synchronization. We rigorously characterize the condition for mounting stealthy attacks under the proposed pulse based interaction mechanism and prove analytically that synchronization of legitimate oscillators can be achieved in the presence of multiple stealthy attackers even when the initial phases are unrestricted, i.e., randomly distributed in the entire oscillation period. This is in distinct difference from most existing attack-resilient synchronization algorithms (including the seminal paper from L. Lamport and P. M. Melliar-Smith, “Synchronizing Clocks in the Presence of Faults,” J. ACM, vol. 32, no. 1, pp. 52-78, 1985), which require a priori (almost) synchronization among legitimate nodes. Numerical simulations are given to confirm the theoretical results.

David Dov;Ronen Talmon;Israel Cohen; "Sequential Audio-Visual Correspondence With Alternating Diffusion Kernels," vol.66(12), pp.3100-3111, June15, 15 2018. A fundamental problem in multimodal signal processing is to quantify relations between two different signals with respect to a certain phenomenon. In this paper, we address this problem from a kernel-based perspective and propose a measure that is based on affinity kernels constructed separately in each modality. This measure is motivated from both a kernel density estimation point of view of predicting the signal in one modality based on the other, as well as from a statistical model, which implies that high values of the proposed measure are expected when signals highly correspond to each other. Considering an online setting, we propose an efficient algorithm for the sequential update of the proposed measure, and demonstrate its application to eye-fixation prediction in audio-visual recordings. The goal is to predict locations within a video recording at which people gaze when watching the video. As studies in psychology imply, people tend to gaze at the location of the audio source, so that their prediction becomes equivalent to locating the audio source within the video. Therefore, we propose to predict eye-fixations as regions within the video with the highest correspondence to the audio signal, thereby demonstrating the improved performance of the proposed method.

Jesus Selva; "Efficient Wideband DOA Estimation Through Function Evaluation Techniques," vol.66(12), pp.3112-3123, June15, 15 2018. This paper presents an efficient evaluation method for the functions involved in the computation of direction-of-arrival (DOA) estimators. The method is a combination of the Chebyshev and barycentric interpolators, and makes use of the discrete cosine transform. We present two applications of this method. The first is for reducing the complexity of the line searches in three wideband DOA estimators: incoherent multiple signal classification, test of orthogonality of projected subspaces, and deterministic maximum likelihood (DML). And the second application is a procedure to compress the wideband DML cost function, so that it is formed by just a few summands. This compression entails a reduction in complexity by a large factor. The evaluation method and its applications are numerically assessed in several numerical examples.

Alican Nalci;Igor Fedorov;Maher Al-Shoukairi;Thomas T. Liu;Bhaskar D. Rao; "Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem," vol.66(12), pp.3124-3139, June15, 15 2018. In this paper, we develop a Bayesian evidence maximization framework to solve the sparse non-negative least squares (S-NNLS) problem. We introduce a family of probability densities referred to as the rectified Gaussian scale mixture (R-GSM) to model the sparsity enforcing prior distribution for the solution. The R-GSM prior encompasses a variety of heavy-tailed densities such as the rectified Laplacian and rectified Student's t-distributions with a proper choice of the mixing density. We utilize the hierarchical representation induced by the R-GSM prior and develop an evidence maximization framework based on the expectation-maximization (EM) algorithm. Using the EM based method, we estimate the hyper-parameters and obtain a point estimate for the solution. We refer to the proposed method as rectified sparse Bayesian learning (R-SBL). We provide four R-SBL variants that offer a range of options for computational complexity and the quality of the E-step computation. These methods include the Markov chain Monte Carlo EM, linear minimum mean-square-error estimation, approximate message passing, and a diagonal approximation. Using numerical experiments, we show that the proposed R-SBL method outperforms existing S-NNLS solvers in terms of both signal and support recovery performance, and is also very robust against the structure of the design matrix.

Gabriel Schamberg;Demba Ba;Todd P. Coleman; "A Modularized Efficient Framework for Non-Markov Time Series Estimation," vol.66(12), pp.3140-3154, June15, 15 2018. We present a compartmentalized approach to finding the maximum a posteriori (MAP) estimate of a latent time series that obeys a dynamic stochastic model and is observed through noisy measurements. We specifically consider modern signal processing problems with non-Markov signal dynamics (e.g., group sparsity) and/or non-Gaussian measurement models (e.g., point process observation models used in neuroscience). Through the use of auxiliary variables in the MAP estimation problem, we show that a consensus formulation of the alternating direction method of multipliers enables iteratively computing separate estimates based on the likelihood and prior and subsequently “averaging” them in an appropriate sense using a Kalman smoother. As such, this can be applied to a broad class of problem settings and only requires modular adjustments when interchanging various aspects of the statistical model. Under broad log-concavity assumptions, we show that the separate estimation problems are convex optimization problems and that the iterative algorithm converges to the MAP estimate. As such, this framework can capture non-Markov latent time series models and non-Gaussian measurement models. We provide example applications involving 1) group-sparsity priors, within the context of electrophysiologic specrotemporal estimation, and 2) non-Gaussian measurement models, within the context of dynamic analyses of learning with neural spiking and behavioral observations.

Santiago Mazuelas;Andrea Conti;Jeffery C. Allen;Moe Z. Win; "Soft Range Information for Network Localization," vol.66(12), pp.3155-3168, June15, 15 2018. The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on distance estimates obtained from measurements (e.g., delay or strength of received waveforms). This paper goes one step further and develops localization techniques that rely on all probable range values rather than on a single estimate of each distance. In particular, the concept of soft range information (SRI) is introduced, showing its essential role for network localization. We then establish a general framework for SRI-based localization and develop algorithms for obtaining the SRI using machine learning techniques. The performance of the proposed approach is quantified via network experimentation in indoor environments. The results show that SRI-based localization techniques can achieve performance approaching the Cramér–Rao lower bound and significantly outperform the conventional techniques especially in harsh wireless environments.

Pouya Ghofrani;Tong Wang;Anke Schmeink; "A Fast Converging Channel Estimation Algorithm for Wireless Sensor Networks," vol.66(12), pp.3169-3184, June15, 15 2018. A set-membership affine projection algorithm is proposed that can estimate a complex-valued channel matrix using a set of complex-valued pilots in the presence of additive white Gaussian noise. It is shown that the algorithm converges faster than the well-known set-membership normalized least mean square algorithm (SM-NLMS) while it resolves the high steady-state error value and the complexity issues in the regular affine projection algorithm. The fast convergence of the proposed algorithm means that a shorter training sequence inside each data block is required, which in turn improves the effective bit rate. This fast convergence is more pronounced when the pilot vectors are highly correlated. We incorporated the set-membership filtering framework into our study to reduce the computational complexity of the algorithm and preserve energy in the WSNs. Other studies have shown the superiority of the adaptive filtering algorithms, and in particular the NLMS algorithm, over other alternatives in various signal processing areas in WSNs, therefore, our proposed algorithm is a powerful substitute for a variety of algorithms. In other words, the implementation of various signal processing algorithms for different purposes can be replaced with the implementation of the proposed multipurpose algorithm. In this paper, we combine the results of our previous studies and prove the convergence of the algorithm. Furthermore, the steady-state analysis in the output mean square error is presented for two cases of pilot signals, and in the conducted simulations, the MSE performance of the algorithm is compared with the regular affine projection algorithm and the SM-NLMS algorithm.

Jiwook Choi;Yunseo Nam;Namyoon Lee; "Spatial Lattice Modulation for MIMO Systems," vol.66(12), pp.3185-3198, June15, 15 2018. This paper proposes spatial lattice modulation (SLM), a spatial modulation method for multiple-input multiple-output (MIMO) systems. The key idea of SLM is to jointly exploit spatial, in-phase, and quadrature dimensions to modulate information bits into a multidimensional signal set that consists of lattice points. One major finding is that SLM achieves a higher spectral efficiency than the existing spatial modulation and spatial multiplexing methods for the MIMO channel under the constraint of M-ary pulse-amplitude modulation input signaling per dimension. In particular, it is shown that when the SLM signal set is constructed by using dense lattices, a significant signal-to-noise-ratio gain, i.e., a nominal coding gain, is attainable compared with the existing methods. In addition, closed-form expressions for the average mutual information of generic SLM are derived under Rayleigh-fading environments. To reduce detection complexity, a low-complexity detection method for SLM, which is referred to as lattice sphere decoding, is developed by exploiting lattice theory. Simulation results verify the accuracy of the conducted analysis and demonstrate that the proposed SLM techniques achieve higher average mutual information and lower ASVEP than do existing methods.

Xinyue Shen;Yuantao Gu; "Nonconvex Sparse Logistic Regression With Weakly Convex Regularization," vol.66(12), pp.3199-3211, June15, 15 2018. In this paper, we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the <inline-formula><tex-math notation="LaTeX">$ell _0$</tex-math></inline-formula> pseudo norm is able to better induce sparsity than the commonly used <inline-formula><tex-math notation="LaTeX">$ell _1$</tex-math></inline-formula> norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding problem and study its local optimality conditions and the choice of the regularization parameter. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then, the general framework is applied to a specific weakly convex function, and a local optimality condition and a bound on the logistic loss at a local optimum are provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and a Nesterov acceleration is used with a convergence guarantee. Its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.

Alireza Sani;Azadeh Vosoughi; "On Distributed Linear Estimation With Observation Model Uncertainties," vol.66(12), pp.3212-3227, June15, 15 2018. We consider distributed estimation of a Gaussian source in a heterogenous bandwidth constrained sensor network, where the source is corrupted by independent multiplicative and additive observation noises. We assume the additive observation noise is zero-mean Gaussian with known variance, however, the system designer is unaware of the distribution of multiplicative observation noise and only knows its first- and second-order moments. For multibit quantizers, we derive an accurate closed-form approximation for the mean-square error (MSE) of the linear minimum MSE) estimator at the fusion center. For both error-free and erroneous communication channels, we propose several rate allocation methods named as longest root to leaf path, greedy, integer relaxation, and individual rate allocation to minimize the MSE given a network bandwidth constraint, and minimize the required network bandwidth given a target MSE. We also derive the Bayesian Cramér–Rao lower bound (CRLB) for an arbitrarily distributed multiplicative observation noise and compare the MSE performance of our proposed methods against the CRLB. Our results corroborate that, for the low-power multiplicative observation noise and adequate network bandwidth, the gaps between the MSE of greedy and integer relaxation methods and the CRLB are negligible, while the MSE of individual rate allocation and uniform methods is not satisfactory. Through analysis and simulations, we also explore why maximum likelihood and maximum a posteriori estimators based on one-bit quantization perform poorly for the low-power additive observation noise.

Vassilis N. Ioannidis;Daniel Romero;Georgios B. Giannakis; "Inference of Spatio-Temporal Functions Over Graphs via Multikernel Kriged Kalman Filtering," vol.66(12), pp.3228-3239, June15, 15 2018. Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations, and offers efficient online reconstruction, even for dynamically evolving network topologies. The kernel-based learning framework bypasses the need for statistical information by capitalizing on the smoothness that graph signals exhibit with respect to the underlying graph. To address the challenge of selecting the appropriate kernel, the proposed filter is combined with a multikernel selection module. Such a data-driven method selects a kernel attuned to the signal dynamics on-the-fly within the linear span of a preselected dictionary. The novel multikernel learning algorithm exploits the eigenstructure of Laplacian kernel matrices to reduce computational complexity. Numerical tests with synthetic and real data demonstrate the superior reconstruction performance of the novel approach relative to state-of-the-art alternatives.

Alec Koppel;Santiago Paternain;Cédric Richard;Alejandro Ribeiro; "Decentralized Online Learning With Kernels," vol.66(12), pp.3240-3255, June15, 15 2018. We consider multiagent stochastic optimization problems over reproducing kernel Hilbert spaces. In this setting, a network of interconnected agents aims to learn decision functions, i.e., nonlinear statistical models, that are optimal in terms of a global convex functional that aggregates data across the network, with only access to locally and sequentially observed samples. We propose solving this problem by allowing each agent to learn a local regression function while enforcing consensus constraints. We use a penalized variant of functional stochastic gradient descent operating simultaneously with low-dimensional subspace projections. These subspaces are constructed greedily by applying orthogonal matching pursuit to the sequence of kernel dictionaries and weights. By tuning the projection-induced bias, we propose an algorithm that allows each individual agent to learn, based on its locally observed data stream and message passing with its neighbors only, a regression function that is close to the globally optimal regression function. That is, we establish that with constant step-size selections agents’ functions converge to a neighborhood of the globally optimal one while satisfying the consensus constraints as the penalty parameter is increased. Moreover, the complexity of the learned regression functions is guaranteed to remain finite. On both multiclass kernel logistic regression and multiclass kernel support vector classification with data generated from class-dependent Gaussian mixture models, we observe an stable function estimation and the state-of-the-art performance for distributed online multiclass classification. Experiments on the Brodatz textures further substantiate the empirical validity of this approach.

Nicholas Tsagkarakis;Panos P. Markopoulos;George Sklivanitis;Dimitris A. Pados; "L1-Norm Principal-Component Analysis of Complex Data," vol.66(12), pp.3256-3267, June15, 15 2018. L1-norm Principal-Component Analysis (L1-PCA) of real-valued data has attracted significant research interest over the past decade. L1-PCA of complex-valued data remains to date unexplored despite the many possible applications (in communication systems, for example). In this paper, we establish theoretical and algorithmic foundations of L1-PCA of complex-valued data matrices. Specifically, we first show that, in contrast to the real-valued case for which an optimal polynomial-cost algorithm was recently reported by Markopoulos, Karystinos, and Pados, complex L1-PCA is formally NP-hard. Then, casting complex L1-PCA as a unimodular optimization problem, we present the first two suboptimal algorithms in the literature for its solution. Extensive experimental studies included in this paper illustrate the sturdy resistance of complex L1-PCA against faulty measurements/outliers in the processed data.

Haoyu Fu;Yuejie Chi; "Quantized Spectral Compressed Sensing: Cramer–Rao Bounds and Recovery Algorithms," vol.66(12), pp.3268-3279, June15, 15 2018. Efficient estimation of wideband spectrum is of great importance for applications such as cognitive radio. Recently, subNyquist sampling schemes based on compressed sensing have been proposed to greatly reduce the sampling rate. However, the important issue of quantization has not been fully addressed, particularly, for high resolution spectrum and parameter estimation. In this paper, we aim to recover spectrally sparse signals and the corresponding parameters, such as frequency and amplitudes, from heavy quantizations of their noisy complex-valued random linear measurements, e.g., only the quadrant information. We first characterize the Cramér–Rao bound under Gaussian noise, which highlights the trade-off between sample complexity and bit depth under different signal-to-noise ratios for a fixed budget of bits. Next, we propose a new algorithm based on atomic norm soft thresholding for signal recovery, which is equivalent to proximal mapping of properly designed surrogate signals with respect to the atomic norm that motivates spectral sparsity. The proposed algorithm can be applied to both the single measurement vector case, as well as the multiple measurement vector case. It is shown that under the Gaussian measurement model, the spectral signals can be reconstructed accurately with high probability, as soon as the number of quantized measurements exceeds the order of <inline-formula><tex-math notation="LaTeX">$Klog n$</tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$K$</tex-math></inline-formula> is the level of spectral sparsity and <inline-formula> <tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the signal dimension. Finally, numerical simulations are provided to validate the proposed approaches.

Marko Angjelichinoski;Anna Scaglione;Petar Popovski;Čedomir Stefanović; "Decentralized DC Microgrid Monitoring and Optimization via Primary Control Perturbations," vol.66(12), pp.3280-3295, June15, 15 2018. We treat the emerging power systems with direct current (dc) microgrids, characterized with high penetration of power electronic converters. We rely on the power electronics to propose a decentralized solution for autonomous learning of and adaptation to the operating conditions of the dc mirogrids; the goal is to eliminate the need to rely on an external communication system for such a purpose. The solution works within the primary droop control loops and uses only local bus voltage measurements. Each controller is able to estimate the generation capacities of power sources, the load demands, and the conductances of the distribution lines. To define a well-conditioned estimation problem, we employ decentralized strategy where the primary droop controllers temporarily switch between operating points in a coordinated manner, following amplitude-modulated training sequences. We study the use of the estimator in a decentralized solution of the optimal economic dispatch problem. The evaluations confirm the usefulness of the proposed solution for autonomous microgrid operation.

Xu Zhang;Wei Cui;Yulong Liu; "Recovery of Structured Signals With Prior Information via Maximizing Correlation," vol.66(12), pp.3296-3310, June15, 15 2018. This paper considers the problem of recovering a structured signal from a relatively small number of noisy measurements with the aid of a similar signal which is known beforehand. We propose a new approach to integrate prior information into the standard recovery procedure by maximizing the correlation between the prior knowledge and the desired signal. We then establish performance guarantees (in terms of the number of measurements) for the proposed method under sub-Gaussian measurements. Specific structured signals including sparse vectors, block-sparse vectors, and low-rank matrices are also analyzed. Furthermore, we present an interesting geometrical interpretation for the proposed procedure. Our results demonstrate that if prior information is good enough, then the proposed approach can (remarkably) outperform the standard recovery procedure. Simulations are provided to verify our results.

Jehyuk Jang;Sanghun Im;Heung-No Lee; "Intentional Aliasing Method to Improve Sub-Nyquist Sampling System," vol.66(12), pp.3311-3326, June15, 15 2018. A modulated wideband converter (MWC) has been introduced as a sub-Nyquist sampler that exploits a set of fast alternating pseudo random (PR) signals. Through parallel analog channels, an MWC compresses a multiband spectrum by mixing it with PR signals in the time domain, and acquires its sub-Nyquist samples. Previously, the ratio of compression was fully dependent on the specifications of PR signals. That is, to further reduce the sampling rate without information loss, faster and longer-period PR signals were needed. However, the implementation of such PR signal generators results in high power consumption and large fabrication area. In this paper, we propose a novel aliased modulated wideband converter (AMWC), which can further reduce the sampling rate of MWC with fixed PR signals. The main idea is to induce intentional signal aliasing at the analog-to-digital converter (ADC). In addition to the first spectral compression by the signal mixer, the intentional aliasing compresses the mixed spectrum once again. We demonstrate that AMWC reduces the number of analog channels and the rate of ADC for lossless sub-Nyquist sampling without needing to upgrade the speed or the period of PR signals. Conversely, for a given fixed number of analog channels and sampling rate, AMWC improves the performance of signal reconstruction.

Naoki Murata;Shoichi Koyama;Norihiro Takamune;Hiroshi Saruwatari; "Sparse Representation Using Multidimensional Mixed-Norm Penalty With Application to Sound Field Decomposition," vol.66(12), pp.3327-3338, June15, 15 2018. A sparse representation method for multidimensional signals is proposed. In generally used group-sparse representation algorithms, the sparsity is imposed only on a single dimension and the signals in the other dimensions are solved in the least-square-error sense. However, multidimensional signals can be sparse in multiple dimensions. For example, in acoustic array processing, in addition to the sparsity of the spatial distribution of acoustic sources, acoustic source signals will also be sparse in the time-frequency domain. We define a multidimensional mixed-norm penalty, which enables the sparsity to be controlled in each dimension. The majorization–minimization approach is applied to derive the optimization algorithm. The proposed algorithm has the advantages of a wide range for the sparsity-controlling parameters, a small cost of adjusting the balancing parameters, and a low computational cost compared with current methods. Numerical experiments indicate that the proposed method is also effective for application to sound field decomposition.

Shan Zhang;Sijia Liu;Vinod Sharma;Pramod K. Varshney; "Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors," vol.66(12), pp.3339-3353, June15, 15 2018. In this paper, we design an optimal sensor collaboration strategy among neighboring nodes while tracking a time-varying parameter using wireless sensor networks in the presence of imperfect communication channels. The sensor network is assumed to be self-powered, where sensors are equipped with energy harvesters that replenish energy from the environment. In order to minimize the mean square estimation error of parameter tracking, we propose an online sensor collaboration policy subject to real-time energy harvesting constraints. The proposed energy allocation strategy is computationally light and only relies on the second-order statistics of the system parameters. For this, we first consider an offline nonconvex optimization problem, which is solved exactly when using semidefinite programming. Based on the offline solution, we design an online power allocation policy that requires minimal online computation and satisfies the dynamics of energy flow at each sensor. We prove that the proposed online policy is asymptotically equivalent to the optimal offline solution and show its convergence rate and robustness. We empirically show that the estimation performance of the proposed online scheme is better than that of the online scheme when channel state information about the dynamical system is available in the low SNR regime. Numerical results demonstrate the effectiveness of our approach.

IEEE Signal Processing Letters - new TOC (2018 May 24) [Website]

Hua Chen;Mona Zehni;Zhizhen Zhao; "A Spectral Method for Stable Bispectrum Inversion With Application to Multireference Alignment," vol.25(7), pp.911-915, July 2018. We focus on an alignment-free method to estimate the underlying signal from a large number of noisy randomly shifted observations. Specifically, we estimate the mean, power spectrum, and bispectrum of the signal from the observations. Since the bispectrum contains the phase information of the signal, reliable algorithms for bispectrum inversion are useful in many applications. We propose a new algorithm using spectral decomposition of the bispectrum phase matrix for this task. For clean signals, we show that the eigenvectors of the bispectrum phase matrix correspond to the true phases of the signal and its shifted copies. In addition, the spectral method is robust to noise. It can be used as a stable and efficient initialization technique for local nonconvex optimization for bispectrum inversion.

Chao Ren;Xiaohai He;Yifei Pu; "Nonlocal Similarity Modeling and Deep CNN Gradient Prior for Super Resolution," vol.25(7), pp.916-920, July 2018. This letter presents a novel super-resolution (SR) method via nonlocal similarity modeling and deep convolutional neural network (CNN) gradient prior (GP). Specifically, on the one hand, the group similarity reliability (GSR) strategy is proposed for improving the adaptive high-dimensional nonlocal total variation (AHNLTV) model [statistical prior, GSR-based AHNLTV (GA)], which captures the structures of the underlying high-resolution (HR) image via the image itself. On the other hand, the GP is learned by using the deep CNN (learned prior), which predicts the gradients from external images. Finally, the GA–GP approach is proposed by incorporating the two complementary priors. The results show that GA–GP achieves better performance than other state-of-the-art SR methods.

Ahmad Reza Heravi;Ghosheh Abed Hodtani; "A New Information Theoretic Relation Between Minimum Error Entropy and Maximum Correntropy," vol.25(7), pp.921-925, July 2018. The past decade has seen the rapid development of information theoretic learning and its applications in signal processing and machine learning. Specifically, minimum error entropy (MEE) and maximum correntropy criterion (MCC) have been widely studied in the literature. Although MEE and MCC are applied in many branches of knowledge and could outperform statistical criteria (such as mean square error), they have not been compared with each other from theoretical point of view. In some cases, MEE and MCC perform similarly to each other; however, under some conditions (e.g., in non-Gaussian environments), they act differently. This letter derives a new information theoretic relation between MEE and MCC, leading to better understanding of the theoretical differences, and illustrates the findings in a common example.

Feng Wang;Jian Cheng;Weiyang Liu;Haijun Liu; "Additive Margin Softmax for Face Verification," vol.25(7), pp.926-930, July 2018. In this letter, we propose a conceptually simple and intuitive learning objective function, i.e., additive margin softmax, for face verification. In general, face verification tasks can be viewed as metric learning problems, even though lots of face verification models are trained in classification schemes. It is possible when a large-margin strategy is introduced into the classification model to encourage intraclass variance minimization. As one alternative, angular softmax has been proposed to incorporate the margin. In this letter, we introduce another kind of margin to the softmax loss function, which is more intuitive and interpretable. Experiments on LFW and MegaFace show that our algorithm performs better when the evaluation criteria are designed for very low false alarm rate.

Seungyoun Lee;Daeyeong Kim;Changick Kim; "Ramp Distribution-Based Image Enhancement Techniques for Infrared Images," vol.25(7), pp.931-935, July 2018. A novel image enhancement method for infrared (IR) images is presented. The proposed method consists of two parts considering the characteristics of high-dynamic-range IR images. First, we attempt to enhance image contrast by introducing the ramp distribution that increases with a constant slope in an ordered histogram domain. The ramp-distributed histogram is incorporated into an optimization problem with a sorted histogram of the input image to calculate a modified histogram. Second, to deal with blurred effects on IR images, we propose a relative edge-strength index for high-boost filtering to effectively suppress noise in relatively uniform regions. Compared with various conventional and state-of-the-art algorithms, the proposed method shows highly competitive performance.

Satoru Emura;Shoko Araki;Tomohiro Nakatani;Noboru Harada; "Distortionless Beamforming Optimized With <inline-formula><tex-math notation="LaTeX">$ell _1$</tex-math> </inline-formula>-Norm Minimization," vol.25(7), pp.936-940, July 2018. We propose a beamforming method that minimizes the <inline-formula><tex-math notation="LaTeX">$ell _1$</tex-math> </inline-formula> norm of a beamformer output vector under the same distortionless constraint as that of the conventional minimum power distortionless response (MPDR) beamformer. Using the <inline-formula> <tex-math notation="LaTeX">$ell _1$</tex-math></inline-formula> norm makes the beamformer output sparse. This leads to reducing the residual elements of the interference signal. In addition, the sensitivity of the proposed beamformer can be controlled by adding a norm constraint as in the MPDR beamformer. The proposed method improved the signal-to-interference-noise ratio by 7 dB from that of the MPDR beamformer for reverberation time <inline-formula><tex-math notation="LaTeX">$T_{60}$</tex-math></inline-formula> = 300 ms in a simulation.

Jinchi Chen;Yulong Liu; "Data-Time Tradeoffs for Corrupted Sensing," vol.25(7), pp.941-945, July 2018. In this letter, we characterize a data-time tradeoff for projected gradient descent (PGD) algorithms used for solving corrupted sensing problems under sub-Gaussian measurements. We also show that with a proper step size, the PGD method can achieve a linear rate of convergence when the number of measurements is sufficiently large.

Jinghui Chu;Jiaqi Zhang;Wei Lu;Xiangdong Huang; "A Novel Multiconnected Convolutional Network for Super-Resolution," vol.25(7), pp.946-950, July 2018. Convolutional neural networks exhibit superior performance for single image super-resolution (SISR) tasks. However, as the network grows deeper, features from the earlier layers are impeded or less used in later layers. In SISR, the earlier layers are mainly composed of local features that are essential to the task. In this letter, we present a novel multiconnected convolutional network for SISR tasks by enhancing the combination of both low- and high-level features. We design a structure built on multiconnected blocks to extract diversified and complicated features via the concatenation of low-level features to high-level features. In addition to stacking multiconnected blocks, a long skip-connection is implemented to further aggregate features of the first layer and a specific later layer. Furthermore, we employ a flexible two-parameter loss function to optimize the training process. The proposed method yields state-of-the-art performance both in terms of quantitative metrics and visual quality. The method also outperforms existing methods on datasets via unknown degrading operators, indicating an excellent generalization ability.

Animesh Yadav;Octavia A. Dobre;H. Vincent Poor; "Is Self-Interference in Full-Duplex Communications a Foe or a Friend?," vol.25(7), pp.951-955, July 2018. This letter studies the potential of harvesting energy from the self-interference of a full-duplex base station. The base station is equipped with a self-interference cancellation switch, which is turned off for a fraction of the transmission period in order to harvest the energy from the self-interference that arises due to the downlink transmission. For the remaining transmission period, the switch is on such that the uplink transmission takes place simultaneously with the downlink transmission. A novel energy-efficiency maximization problem is formulated for the joint design of downlink beamformers, uplink power allocations, and the transmission time-splitting factor. The optimization problem is nonconvex, and hence, a rapidly converging iterative algorithm is proposed by employing the successive convex approximation approach. Numerical simulation results show significant improvement in the energy-efficiency by allowing self-energy recycling.

Zhichao Sheng;Hoang Duong Tuan;Trung Q. Duong;H. Vincent Poor; "Outage-Aware Secure Beamforming in MISO Wireless Interference Networks," vol.25(7), pp.956-960, July 2018. Based on the knowledge of the channel distributions of a multi-input single-output wireless network of multiple transmitter-user pairs overheard by an eavesdropper, this letter develops an outage-aware beamforming design to optimize the users’ quality-of-service (QoS) in terms of their secrecy rates. This is a very computationally difficult problem with a nonconcave objective function and nonlinear equality constraints in beamforming vectors. A path-following algorithm of low-complexity and rapid convergence is proposed for computation, which is also extended to solving the problem of maximizing the network's secure energy efficiency under users’ QoS constraints. Numerical examples are provided to verify the efficiency of the proposed algorithms.

Mihai I. Florea;Adrian Basarab;Denis Kouamé;Sergiy A. Vorobyov; "An Axially Variant Kernel Imaging Model Applied to Ultrasound Image Reconstruction," vol.25(7), pp.961-965, July 2018. Existing ultrasound deconvolution approaches unrealistically assume, primarily for computational reasons, that the convolution model relies on a spatially invariant kernel and circulant boundary conditions. We discard both restrictions and introduce an image formation model applicable to ultrasound imaging and deconvolution based on an axially varying kernel, which accounts for arbitrary boundary conditions. Our model has the same computational complexity as the one employing spatially invariant convolution and has negligible memory requirements. To accommodate the state-of-the-art deconvolution approaches when applied to a variety of inverse problem formulations, we also provide an equally efficient adjoint expression for our model. Simulation results confirm the tractability of our model for the deconvolution of large images. Moreover, in terms of accuracy metrics, the quality of reconstruction using our model is superior to that obtained using spatially invariant convolution.

Chih-Peng Li;Kuo-Jen Chang;Ho-Hsuan Chang;Yen-Ming Chen; "Perfect Sequences of Odd Prime Length," vol.25(7), pp.966-969, July 2018. A sequence is said to be perfect if it has an ideal periodic autocorrelation function. In addition, the degree of a sequence is defined as the number of distinct nonzero elements within each period of the sequence. This study presents a systematic method for constructing perfect sequences (PSs) of odd prime periods, where the general constraint equations for the sequence coefficients are derived, providing a solid theoretical foundation to the construction of PSs. The proposed scheme commences by partitioning a cyclic group <inline-formula><tex-math notation="LaTeX">$mathbf {Z}_P=lbrace 1,2, ldots,P-1rbrace$</tex-math></inline-formula> into <inline-formula><tex-math notation="LaTeX">$K$ </tex-math></inline-formula> cosets of cardinality <inline-formula><tex-math notation="LaTeX">$M$</tex-math> </inline-formula>, where <inline-formula><tex-math notation="LaTeX">$P=Kcdot M+1$</tex-math></inline-formula> is an odd prime. Based on this partition, the degree-(<inline-formula><tex-math notation="LaTeX">$K$</tex-math> </inline-formula> + 1) PSs are then constructed. Finally, case studies are presented to illustrate the proposed construction.

Avik Ranjan Adhikary;Sudhan Majhi;Zilong Liu;Yong Liang Guan; "New Sets of Even-Length Binary Z-Complementary Pairs With Asymptotic ZCZ Ratio of <inline-formula> <tex-math notation="LaTeX">$3/4$</tex-math></inline-formula>," vol.25(7), pp.970-973, July 2018. This letter is focused on increasing the zero correlation zone (ZCZ) of even-length binary Z-complementary pairs (EB-ZCPs). Till date, the maximum ZCZ ratio (i.e., ZCZ width over the sequence length) for systematically constructed EB-ZCPs is <inline-formula><tex-math notation="LaTeX">$2/3$</tex-math></inline-formula>. In this letter, we give a construction of EB-ZCPs with lengths <inline-formula><tex-math notation="LaTeX">$2^{alpha +2} 10^beta 26^gamma +2$ </tex-math></inline-formula> (where <inline-formula><tex-math notation="LaTeX">$alpha$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$beta$</tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$gamma$</tex-math></inline-formula> are nonnegative integers) and ZCZ widths <inline-formula><tex-math notation="LaTeX">$3 times 2^alpha 10^beta 26^gamma +1$</tex-math></inline-formula>, thus achieving asymptotic ZCZ ratio of <inline-formula><tex-math notation="LaTeX">$3/4$</tex-math></inline-formula>. The proposed EB-ZCPs are constructed via proper insertion of concatenated odd-length binary ZCPs. The ZCZ width is proved by exploiting several newly identified intrinsic structure properties of binary Golay complementary pairs, obtained from Turyn's method. The proposed EB-ZCPs have aperiodic autocorrelation sums (AACS) magnitude of 4 outside the ZCZ region (except for the last time-shift taking AACS value of zero).

IEEE Journal of Selected Topics in Signal Processing - new TOC (2018 May 24) [Website]

* "Table of Contents," vol.12(2), pp.251-252, May 2018.*

C. Masouros;M. Sellathurai;C. B. Papadias;L. Dai;W. Yu;T. Sizer; "Introduction to the Issue on Hybrid Analog–Digital Signal Processing for Hardware-Efficient Large-Scale Antenna Arrays (Part I)," vol.12(2), pp.253-255, May 2018.

Zihuan Wang;Ming Li;Qian Liu;A. Lee Swindlehurst; "Hybrid Precoder and Combiner Design With Low-Resolution Phase Shifters in mmWave MIMO Systems," vol.12(2), pp.256-269, May 2018. Millimeter-wave (mmWave) communications have been considered as a key technology for next-generation cellular systems and Wi-Fi networks because of its advances in providing orders-of-magnitude wider bandwidth than current wireless networks. Economical and energy-efficient analog/digital hybrid precoding and combining transceivers have been often proposed for mmWave massive multiple-input multiple-output (MIMO) systems to overcome the severe propagation loss of mmWave channels. One major shortcoming of existing solutions lies in the assumption of infinite or high-resolution phase shifters (PSs) to realize the analog beamformers. However, low-resolution PSs are typically adopted in practice to reduce the hardware cost and power consumption. Motivated by this fact, in this paper, we investigate the practical design of hybrid precoders and combiners with low-resolution PSs in mmWave MIMO systems. In particular, we propose an iterative algorithm which successively designs the low-resolution analog precoder and combiner pair, aiming at conditionally maximizing the spectral efficiency. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further enhance the spectral efficiency. In an effort to achieve an even more hardware-efficient large antenna array, we also investigate the design of hybrid beamformers with one-bit resolution (binary) PSs, and present a novel binary analog precoder and combiner optimization algorithm. After analyzing the computational complexity, the proposed low-resolution hybrid beamforming design is further extended to multiuser MIMO communication systems. Simulation results demonstrate the performance advantages of the proposed algorithms compared to existing low-resolution hybrid beamforming designs, particularly for the one-bit resolution PSs scenario.

Miguel Ángel Vázquez;Luis Blanco;Ana I. Pérez-Neira; "Spectrum Sharing Backhaul Satellite-Terrestrial Systems via Analog Beamforming," vol.12(2), pp.270-281, May 2018. Current satellite and terrestrial backhaul systems are deployed in disjoint frequency bands. This fact precludes an efficient use of the spectrum and limits the evolution of wireless backhauling networks. In this paper, we propose an interference mitigation technique in order to allow the spectrum coexistence between satellite and terrestrial backhaul links. This interference reliever is implemented at the terrestrial backhaul nodes, which are assumed to be equipped with multiple antennas. Due to the large bandwidth and huge number of antennas required in these systems, we consider pure analog beamforming. Precisely, we assume a phased array beamforming configuration so that the terrestrial backhaul node can only reduce the interference by changing the phases of each beamforming weight. Two cases are considered: the 18 and 28 GHz band where transmit and receive beamforming optimization problems shall be tackled, respectively. In both cases, the optimization problem results in a nonconvex problem that we propose to solve via two alternative convex approximation methods. These two approaches are evaluated and they present less than 1 dB array gain loss with respect to the upper bound solution. Finally, the spectral efficiency gains of the proposed spectrum sharing scenarios are validated in numerical simulations.

Xianghao Yu;Jun Zhang;Khaled B. Letaief; "A Hardware-Efficient Analog Network Structure for Hybrid Precoding in Millimeter Wave Systems," vol.12(2), pp.282-297, May 2018. Hybrid precoding has been recently proposed as a cost-effective transceiver solution for millimeter wave systems. While the number of radio frequency chains has been effectively reduced in existing works, a large number of high-precision phase shifters are still needed. Practical phase shifters are with coarsely quantized phases, and their number should be reduced to a minimum due to cost and power consideration. In this paper, we propose a novel hardware-efficient implementation for hybrid precoding, called the fixed phase shifter (FPS) implementation. It only requires a small number of phase shifters with quantized and fixed phases. To enhance the spectral efficiency, a switch network is put forward to provide dynamic connections from phase shifters to antennas, which is adaptive to the channel states. An effective alternating minimization algorithm is developed with closed-form solutions in each iteration to determine the hybrid precoder and the states of switches. Moreover, to further reduce the hardware complexity, a group-connected mapping strategy is proposed to reduce the number of switches. Simulation results show that the FPS fully-connected hybrid precoder achieves higher hardware efficiency with much fewer phase shifters than existing proposals. Furthermore, the group-connected mapping achieves a good balance between spectral efficiency and hardware complexity.

Lucas N. Ribeiro;Stefan Schwarz;Markus Rupp;André L. F. de Almeida; "Energy Efficiency of mmWave Massive MIMO Precoding With Low-Resolution DACs," vol.12(2), pp.298-312, May 2018. With the congestion of the sub-6 GHz spectrum, the interest in massive multiple-input multiple-output (MIMO) systems operating on millimeter wave spectrum grows. In order to reduce the power consumption of such massive MIMO systems, hybrid analog/digital transceivers and application of low-resolution digital-to-analog/analog-to-digital converters have been recently proposed. In this work, we investigate the energy efficiency of quantized hybrid transmitters equipped with a fully/partially connected phase-shifting network composed of active/passive phase shifters and compare it to that of quantized digital precoders. We introduce a quantized single-user MIMO system model based on an additive quantization noise approximation considering realistic power consumption and loss models to evaluate the spectral and energy efficiencies of the transmit precoding methods. Simulation results show that partially connected hybrid precoders can be more energy-efficient compared to digital precoders, while fully connected hybrid precoders exhibit poor energy efficiency in general. Also, the topology of phase-shifting components offers an energy-spectral efficiency tradeoff: Active phase shifters provide higher data rates, while passive phase shifters maintain better energy efficiency.

Rongbin Guo;Yunlong Cai;Minjian Zhao;Qingjiang Shi;Benoit Champagne;Lajos Hanzo; "Joint Design of Beam Selection and Precoding Matrices for mmWave MU-MIMO Systems Relying on Lens Antenna Arrays," vol.12(2), pp.313-325, May 2018. Wireless transmission relying on lens antenna arrays is becoming more and more attractive for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems using a limited number of radio frequency chains due to the lens’ energy-focusing capability. In this paper, we consider the joint design of the beam selection and precoding matrices in order to maximize the sum-rate of a downlink single-sided lens MU-MIMO mmWave system under transmit power constraints. We first formulate the optimization problem into a tractable form using the popular weighted minimum mean squared error (WMMSE) approach. To solve this problem, we then propose an efficient joint beam selection and precoding design algorithm based on the innovative penalty dual decomposition method. To reduce the design complexity, we also propose a simplified algorithm by combining the interference-aware beam selection scheme with the WMMSE approach. Simulation results demonstrate that our proposed algorithms can converge in a few iterations and achieve near-optimal performance when compared to the fully digital precoding scheme, thus enabling them to outperform the competing methods.

Hsiao-Lan Chiang;Wolfgang Rave;Tobias Kadur;Gerhard Fettweis; "Hybrid Beamforming Based on Implicit Channel State Information for Millimeter Wave Links," vol.12(2), pp.326-339, May 2018. Hybrid beamforming provides a promising solution to achieve high data rate transmission at millimeter waves. Implementing hybrid beamforming at a transceiver based on available channel state information is a common solution. However, many reference methods ignore the complexity of channel estimation for large antenna arrays or subsequent steps, such as the singular value decomposition of a channel matrix. To this end, we present a low-complexity scheme that exploits implicit channel knowledge to facilitate the design of hybrid beamforming for frequency-selective fading channels. The implicit channel knowledge can be interpreted as couplings between all possible pairs of analog beamforming vectors at the transmitter and receiver over the surrounding channel. Instead of calculating mutual information between large antenna arrays, we focus on small-size coupling matrices between beam patterns selected by using appropriate key parameters as performance indicators. This converts the complicated hybrid beamforming problem to a much simpler one: it amounts to collecting different sets of the large-power coupling coefficients to construct multiple alternatives for an effective channel matrix. Then, the set yielding the largest Frobenius norm (or the largest absolute value of the determinant) of the effective channel provides the solution to the hybrid beamforming problem. It turns out that the proposed method does not require information on MIMO channel and can be simply implemented by the received correlated pilot signals that are supposed to be used for channel estimation.

Yin Long;Zhi Chen;Jun Fang;Chintha Tellambura; "Data-Driven-Based Analog Beam Selection for Hybrid Beamforming Under mm-Wave Channels," vol.12(2), pp.340-352, May 2018. Hybrid beamforming is a promising low-cost solution for large multiple-input multiple-output systems, where the base station is equipped with fewer radio frequency chains. In these systems, the selection of codewords for analog beamforming is essential to optimize the uplink sum rate. In this paper, based on machine learning, we propose a data-driven method of analog beam selection to achieve a near-optimal sum rate with low complexity, which is highly dependent on training data. Specifically, we take the beam selection problem as a multiclass-classification problem, where the training dataset consists of a large number of samples of the millimeter-wave channel. Using this training data, we exploit the support vector machine algorithm to obtain a statistical classification model, which maximizes the sum rate. For real-time transmissions, with the derived classification model, we can select, with low complexity, the optimal analog beam of each user. We also propose a novel method to determine the optimal parameter of Gaussian kernel function via McLaughlin expansion. Analysis and simulation results reveal that, as long as the training data are sufficient, the proposed data-driven method achieves a near-optimal sum-rate performance, while the complexity reduces by several orders of magnitude, compared to the conventional method.

José P. González-Coma;Javier Rodríguez-Fernández;Nuria González-Prelcic;Luis Castedo;Robert W. Heath; "Channel Estimation and Hybrid Precoding for Frequency Selective Multiuser mmWave MIMO Systems," vol.12(2), pp.353-367, May 2018. Configuring the hybrid precoders and combiners in a millimeter wave multiuser multiple-input multiple-output system is challenging in frequency selective channels. In this paper, we develop a system that uses compressive estimation on the uplink to configure precoders and combiners for the downlink. In the first step, the base station (BS) simultaneously estimates the channels from all the mobile stations on each subcarrier. To reduce the number of measurements required, compressed sensing techniques are developed that exploit common support on the different subcarriers. In the second step, exploiting reciprocity and the channel estimates the BS designs hybrid precoders and combiners. Two algorithms are developed for this purpose, with different performance and complexity tradeoffs: First, a factorization of the purely digital solution; and second, an iterative hybrid design. Extensive numerical experiments evaluate the proposed solutions comparing to the state-of-the-art strategies, and illustrating design tradeoffs in overhead, complexity, and performance.

Miguel R. Castellanos;Vasanthan Raghavan;Jung H. Ryu;Ozge H. Koymen;Junyi Li;David J. Love;Borja Peleato; "Channel-Reconstruction-Based Hybrid Precoding for Millimeter-Wave Multi-User MIMO Systems," vol.12(2), pp.383-398, May 2018. The focus of this paper is on multi-user multi-input multi-output transmissions for millimeter-wave systems with a hybrid precoding architecture at the base station. To enable multiuser transmissions, the base station uses a cell-specific codebook of beamforming vectors over an initial beam alignment phase. Each user uses a user-specific codebook of beamforming vectors to learn the top-<inline-formula><tex-math notation="LaTeX">$P$ </tex-math></inline-formula> (where <inline-formula><tex-math notation="LaTeX">$P geq 1$</tex-math></inline-formula>) beam pairs in terms of the observed signal-to-noise ratio (<inline-formula><tex-math notation="LaTeX">${text{SNR}}$ </tex-math></inline-formula>) in a single-user setting. The top-<inline-formula><tex-math notation="LaTeX">$P$ </tex-math></inline-formula> beam indices along with their <inline-formula><tex-math notation="LaTeX">${text{SNR}}$ </tex-math></inline-formula>s are fed back from each user and the base station leverages this information to generate beam weights for simultaneous transmissions. A typical method to generate the beam weights is to use only the best beam for each user and either steer energy along this beam, or to utilize this information to reduce multi-user interference. The other beams are used as fall-back options to address blockage or mobility. Such an approach completely discards information learned about the channel condition(s) even though each user feeds back this information. With this background, this paper develops an advanced directional precoding structure for simultaneous transmissions at the cost of an additional marginal feedback overhead. This construction relies on three main innovations: first, additional feedback to allow the base station to reconstruct a rank- <inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula> approximation of the channel matrix between it and each user; se- ond, a zero-forcing structure that leverages this information to combat multi-user interference by remaining agnostic of the receiver beam knowledge in the precoder design; and third, a hybrid precoding architecture that allows both amplitude and phase control at low complexity and cost to allow the implementation of the zero-forcing structure. Numerical studies show that the proposed scheme results in a significant sum rate performance improvement over naïve schemes even with a coarse initial beam alignment codebook.

Yuanquan Hong;Xiaojun Jing;Hui Gao; "Programmable Weight Phased-Array Transmission for Secure Millimeter-Wave Wireless Communications," vol.12(2), pp.399-413, May 2018. We propose a novel programmable weight phased array (PWPA) architecture and the corresponding schemes for secure millimeter-wave communications. PWPA consists of a conventional phased-array architecture followed by a programable power amplifier used to change the weights of amplitude of antenna element. We propose new schemes of inverted antenna subset transmission technique and optimized weight antenna subset transmission technique based on PWPA, which generate more artificial noise than conventional schemes in the undesired directions. We provide performance evaluation in infinite resolution and finite resolution of phase shifters. Aiming to reduce the sidelobe level, a simulated annealing particle swarm optimization based algorithm is proposed to construct a smaller codebook, which achieves the sidelobe attenuation of 21.12 dB and 5.1 dB in a specific undesired direction and all undesired directions, respectively. Theoretical and simulation results show that the proposed solutions provide superior physical layer security performance. The average symbol error rate and secrecy capacity of the proposed schemes achieve higher robustness against eavesdropper's gain and transmission distance. Moreover, the secrecy capacity of the proposed scheme is stable within 1.5 degrees of phase error of PSs, and the performance gain over the existing schemes is about <inline-formula><tex-math notation="LaTeX">$33.0%$</tex-math></inline-formula>.

IEEE Signal Processing Magazine - new TOC (2018 May 24) [Website]

* "Front Cover," vol.35(3), pp.C1-C1, May 2018.* Presents the front cover for this issue of the publication.

* "GlobalSIP," vol.35(3), pp.C2-C2, May 2018.* Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

* "Table of Contents," vol.35(3), pp.1-2, May 2018.* Presents the table of contents for this issue of the publication.

* "Masthead," vol.35(3), pp.2-2, May 2018.* Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Robert W. Heath; "Introducing the New Editorial Team of IEEE Signaling Processing Magazine [From the Editor[Name:_blank]]," vol.35(3), pp.4-5, May 2018. Presents information on the new editorial team for this issue of the publication.

Ali H. Sayed; "Galileo, Fourier, and Openness in Science [President's Message[Name:_blank]]," vol.35(3), pp.6-8, May 2018. Presents the President’s message for this issue of the publication.

Anna Scaglione; "Independence [Humor[Name:_blank]]," vol.35(3), pp.9-9, May 2018. Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Namrata Vaswani; "Women in Signal Processing Committee Highlights [Society News[Name:_blank]]," vol.35(3), pp.10-10, May 2018. Reports on the activities of the SPS society's Women in Signal Processing (WISP) Subcommittee.

John Edwards; "The "Light" Side of Signal Processing: Research Teams Work Toward a Signal Processing-Enabled Photonics Future [Special Reports[Name:_blank]]," vol.35(3), pp.11-14, May 2018. Discusses research conducted at the Massachusetts Institute of Technology (MIT) where engineers and scientists are investigating the possibility of using light rather than wires to provide communication across different parts of a microchip. As the size and speed of many conventional electronics technologies begin reaching their practical limits, a growing number of researchers is turning their attention to photonics and related optical-based approaches to continue the push toward smaller, faster, cheaper, and more innovative computer and communication devices. Building on an already lengthy and impressive track record, signal processing is now playing a critical role in the development of new optical communication processes. Digital signal processing (DSP), for instance, is widely viewed as the catalyst for the “coherent revolution” that paved the way for optical telecom networks in the mid-2000s. Now, signal processing is playing a role in research that promises to lead to denser and faster microchips, more efficient networks, and other important technology innovations.

* "Asilomar CFP," vol.35(3), pp.15-15, May 2018.* Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

Alon Kipnis;Yonina C. Eldar;Andrea J. Goldsmith; "Analog-to-Digital Compression: A New Paradigm for Converting Signals to Bits," vol.35(3), pp.16-39, May 2018. Processing, storing, and communicating information that originates as an analog signal involves converting this information to bits. This conversion can be described by the combined effect of sampling and quantization, as shown in Figure 1. The digital representation is achieved by first sampling the analog signal to represent it by a set of discretetime samples and then quantizing these samples to a finite number of bits. Traditionally, these two operations are considered separately. The sampler is designed to minimize the information loss due to sampling based on characteristics of the continuoustime input. The quantizer is designed to represent the samples as accurately as possible, subject to a constraint on the number of bits that can be used in the representation. The goal of this article is to revisit this paradigm by illuminating the dependency between these two operations. In particular, we explore the requirements of the sampling system subject to the constraints on the available number of bits for storing, communicating, or processing the analog information.

Zhijin Qin;Jiancun Fan;Yuanwei Liu;Yue Gao;Geoffrey Ye Li; "Sparse Representation for Wireless Communications: A Compressive Sensing Approach," vol.35(3), pp.40-58, May 2018. Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with a focus on the most recent compressive sensing (CS)-enabled approaches. With the help of the sparsity property, CS is able to enhance the spectrum efficiency (SE) and energy efficiency (EE) of fifth-generation (5G) and Internet of Things (IoT) networks.

Beibei Wang;Qinyi Xu;Chen Chen;Feng Zhang;K.J. Ray Liu; "The Promise of Radio Analytics: A Future Paradigm of Wireless Positioning, Tracking, and Sensing," vol.35(3), pp.59-80, May 2018. With the proliferation of Internet of Things (IoT) applications, billions of household appliances, phones, smart devices, security systems, environment sensors, vehicles, buildings, and other radio-connected devices will transmit data and communicate with each other or people, and it will be possible to constantly measure and track virtually everything. Among the various approaches to measuring what is happening in the surrounding environment, wireless sensing has received increasing attention in recent years because of the ubiquitous deployment of wireless radio devices. In addition, human activities affect wireless signal propagation, so understanding and analyzing how these signals react to human activities can reveal rich information about the activities around us.

Mahmoud Hassan;Fabrice Wendling; "Electroencephalography Source Connectivity: Aiming for High Resolution of Brain Networks in Time and Space," vol.35(3), pp.81-96, May 2018. The human brain is a large-scale network the function of which depends on dynamic interactions between spatially distributed regions. In the rapidly evolving field of network neuroscience, two unresolved challenges hold the promise of potential breakthroughs. First, functional brain networks should be identified using noninvasive and easy-to-use neuroimaging techniques. Second, the time-space resolution of these techniques should be good enough to assess the dynamics of the identified networks. Emerging evidence suggests that the electroencephalography (EEG) source-connectivity method may offer solutions to both issues, provided that scalp EEG signals are appropriately processed. Therefore, this technique's performance strongly depends on signal processing that involves various methods, such as preprocessing approaches, inverse solutions, statistical couplings between signals, and network science.

Milos Cernak;Afsaneh Asaei;Alexandre Hyafil; "Cognitive Speech Coding: Examining the Impact of Cognitive Speech Processing on Speech Compression," vol.35(3), pp.97-109, May 2018. Speech coding is a field in which compression paradigms have not changed in the last 30 years. Speech signals are most commonly encoded with compression methods that have roots in linear predictive theory dating back to the early 1940s. This article bridges this influential theory with recent cognitive studies applicable in speech communication engineering. It reviews the mechanisms of speech perception that have led to perceptual speech coding. The focus is on human speech communication and machine learning and the application of cognitive speech processing in speech compression that presents a paradigm shift from perceptual (auditory) speech processing toward cognitive (auditory plus cortical) speech processing.

Sithan Kanna;Wilhelm von Rosenberg;Valentin Goverdovsky;Anthony G. Constantinides;Danilo P. Mandic; "Bringing Wearable Sensors into the Classroom: A Participatory Approach [SP Education[Name:_blank]]," vol.35(3), pp.110-130, May 2018. By bringing research into the curriculum, this article explores new opportunities to refresh some classic signal processing courses. Since 2015, we in the Electrical and Electronic Engineering (EEE) Department of Imperial College London, United Kingdom, have explored the extent to which the level of student engagement and learning can be enhanced by inviting the students to perform signal processing exercises on their own physiological data. More specifically, using new wearable sensor technology and video instructions as an experiment guide, the students are asked to record their electrocardiograms (ECGs) and perform both time- and spectral-domain estimation tasks on their own real-world data. In this way, the students not only gain experience with recording hardware and sources of signal contamination (baseline wanders and artifacts), but they also are highly motivated by being kept in the loop and through their part ownership of their course.

* "SLT Call for Papers," vol.35(3), pp.117-117, May 2018.* Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Marcelo G.S. Bruno;Stiven S. Dias; "A Bayesian Interpretation of Distributed Diffusion Filtering Algorithms [Lecture Notes[Name:_blank]]," vol.35(3), pp.118-123, May 2018. In this lecture note, we use a Bayesian methodology to formulate the optimal solution to the problem of cooperative tracking of a time-varying signal over a partially connected network of multiple agents, where each agent has sensing, processing, and communication capabilities of its own. Subsequently, assuming a general state-space model for the hidden state vectors and the agents? observations, we present, also from a Bayesian perspective, the general form of the adaptthen-combine (ATC) and the random exchange (RndEx) distributed diffusion filters, contrasting them with the ideal, optimal network filter.

Balazs Bank; "Converting Infinite Impulse Response Filters to Parallel Form [Tips & Tricks[Name:_blank]]," vol.35(3), pp.124-130, May 2018. Discrete-time rational transfer functions are often converted to parallel second-order sections due to better numerical performance compared to direct form infinite impulse response (IIR) implementations. This is usually done by performing partial fraction expansion over the original transfer function. When the order of the numerator polynomial is greater or equal to that of the denominator, polynomial long division is applied before partial fraction expansion resulting in a parallel finite impulse response (FIR) path. This article shows that applying this common procedure can cause a severe dynamic range limitation in the filter because the individual responses can be much larger than the net transfer function. This can be avoided by applying a delayed parallel form where the response of the second-order sections is delayed in such a way that there is no overlap between the IIR and FIR parts. In addition, a simple least-squares procedure is presented to perform the conversion that is numerically more robust than the usual Heaviside partial fraction expansion. Finally, the possibilities of converting series second-order sections to the delayed parallel form are discussed.

* "[Dates Ahead[Name:_blank]]," vol.35(3), pp.132-132, May 2018.* Presents information on SPS future events and meetings.

* "ICASSP 2019," vol.35(3), pp.C3-C3, May 2018.* Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

IET Signal Processing - new TOC (2018 May 24) [Website]

Geethanjali Purushothaman;Prashanth R. Prakash;Saurabh Kothari; "Investigation of multiple frequency recognition from single-channel steady-state visual evoked potential for efficient brain–computer interfaces application," vol.12(3), pp.255-259, 5 2018. In this study, the authors have examined a single-channel electroencephalogram from Oz for identification of seven visual stimuli frequencies with multivariate synchronisation index (MSI) and canonical correlation analysis (CCA). Authors investigated the feasibility in three case studies with varying overlapped as well as non-overlapped window lengths. The visual stimuli frequencies ≤10 Hz are considered in case study I and >10 Hz in case study II. Case study III contains frequencies of both case studies I and II. All the case studies revealed that CCA outperforms MSI for reference signals constituting fundamental, one subharmonics, and three super-harmonics. The results revealed that the accuracy of identification improves with 50% overlap in both the algorithms. Further, recognition accuracy is studied with varying combination sub- and super-harmonics for case study III with 50% overlap. The results revealed that CCA and MSI perform better with reference signals constituting fundamental and twice fundamental frequency compared with traditional power spectral density analysis (PSDA). In addition to recognition accuracy, the information bit transfer rate is also higher in CCA relative to MSI and PSDA.

Md. Shariful Alam;Wissam A. Jassim;Muhammad S.A. Zilany; "Radon transform of auditory neurograms: a robust feature set for phoneme classification," vol.12(3), pp.260-268, 5 2018. Classification of speech phonemes is challenging, especially under noisy environments, and hence traditional speech recognition systems do not perform well in the presence of noise. Unlike traditional methods in which features are mostly extracted from the properties of the acoustic signal, this study proposes a new feature for phoneme classification using neural responses from a physiologically based computational model of the auditory periphery. The two-dimensional neurogram was constructed from the simulated responses of auditory-nerve fibres to speech phonemes. Features of neurogram images were extracted using the Discrete Radon Transform, and the dimensionality of features was reduced using an efficient feature selection technique. A standard classifier, Support Vector Machine, was employed to model and test the phoneme classes. Classification performance was evaluated in quiet and under noisy conditions in which test data were corrupted with various environmental distortions such as additive noise, room reverberation, and telephone-channel noise. Performances were also compared with the results from existing methods such as the Mel-frequency cepstral coefficient, Gammatone frequency cepstral coefficient, and frequency-domain linear prediction-based phoneme classification methods. In general, the proposed neural feature exhibited a better classification accuracy in quiet and under noisy conditions compared with the performance of most existing acoustic-signal-based methods.

Xin Liu;Meng Xu;Shu-Juan Peng;Wentao Fan;Ji-Xiang Du; "Efficient human motion capture data annotation via multi-view spatiotemporal feature fusion," vol.12(3), pp.269-276, 5 2018. The availability of large motion capture (mocap) data has sparked a great motivation for computer animation, and the task of automatically annotating complex mocap sequences plays an important role in the efficient motion analysis. To this end, this study presents an efficient human mocap data annotation approach by using multi-view spatiotemporal feature fusion. First, the authors exploit an improved hierarchical aligned cluster analysis algorithm to divide the unknown human mocap sequence into several sub-motion clips, and each sub-motion clip incorporates a particular semantic meaning. Then, the two kinds of multi-view features, namely most informative central distances and most informative geometric angles, are discriminatively extracted and temporally modelled by a Fourier temporal pyramid to complementarily characterise each motion clip. Finally, the authors utilise the discriminant correlation analysis to fuse these two types of motion features and further employ an extreme learning machine to annotate each sub-motion clip. The extensive experiments tested on the public available database have demonstrated the effectiveness of the proposed approach in comparison with the existing counterparts.

Armin Ghani;Firooz Keyvani;Seyed Hassan Sedighy; "Antenna array placement on limited bound for isotropic and optimal direction-of-arrival estimation," vol.12(3), pp.277-283, 5 2018. An optimal placement for planar antenna array elements is proposed to achieve a precise direction of arrival (DOA) estimation on a limited bound. For this purpose, the Cramer-Rao lower bound is chosen as a suitable and useful criterion. In order to have an accurate isotropic DOA estimation, the placement of the antenna elements in the array configuration is achieved by using the invasive weed optimisation algorithm. Moreover, the optimisation results show higher efficiency compared with the similar uniform circular array. It is proved that the optimised arrays are ambiguity free, also.

Zhiyuan Yin;Yan Zhou;Yongxin Li; "Atom-based-segmented MP compression algorithm for seismic data," vol.12(3), pp.284-293, 5 2018. Data compression is an effective way to improve the seismic data transmission efficiency. The features of seismic exploration are long sampling time and large data quantity, so the compression algorithm should achieve high-fidelity, high-compression ratio (CR) and low-compression time. Since the existing compression algorithms cannot meet the requirements of site-collected seismic data compression, the segmented matching pursuit (SMP) compression algorithm based on new atom dictionary is proposed. This method is based on the principle of MP. A novel-segmented compression structure is adopted. The modified Morlet wave atom dictionary is designed to replace the previous dictionaries. The results of comparative experiments show that the proposed algorithm makes improvements in CR and compression time with the same fidelity requirement.

Jun Zhang;Guisheng Liao;Shengqi Zhu;Jingwei Xu;Feiyang Liu; "Wavenumber-domain autofocus algorithm for helicopter-borne rotating synthetic aperture radar," vol.12(3), pp.294-300, 5 2018. Helicopter-borne rotating synthetic aperture radar (ROSAR) enjoys fast imaging property as the synthetic aperture obtained by rotor rotation. However, ROSAR data processing is usually a challenging task due to severe range cell migration (RCM) and motion error. To solve this problem, an enhanced phase gradient autofocus (PGA) algorithm based on helicopter-borne ROSAR wavenumber-domain imaging approach is proposed in this study, which alleviates the imaging performance degradation due to the RCM and motion error. With even small motion error induced in the wavenumber domain, the influence of motion error will become evident after performing Stolt interpolation, which induces serious image defocusing and degradation. Hence, the authors further propose the PGA-based ROSAR motion compensation scheme, which combines quadratic term correction with phase gradient estimation, and well-focused ROSAR images can be obtained via iterative processing. Several results of simulated experiments are presented to validate the proposed method for helicopter-borne ROSAR imaging.

Waseem Waheed;David B.H. Tay; "Graph polynomial filter for signal denoising," vol.12(3), pp.301-309, 5 2018. A technique for denoising signals defined over graphs was recently proposed by Chen et al. (2014). The technique is based on a regularisation framework and denoising is achieved by solving an optimisation problem. Matrix inversion is required and an approximate solution that avoids directly calculating the inverse, by using a graph filter, was proposed by Chen et al. (2014). The technique, however, requires an eigendecomposition and the resulting filter degree is high. In this study, the authors propose a computationally efficient technique that is based on a least squares approximation of the eigenvalues of the inverse. They show that a good approximation can be achieved with a low degree graph polynomial filter without the need for any eigendecomposition. Low degree filters also have the desirable property of vertex localisation (analogous to time localisation). The filter gives denoising results that are very similar to that using the exact solution and can be implemented using distributed processing.

Mohammad Hamdollahzadeh;Sadjad Adelipour;Fereydoon Behnia; "Sequential sensor placement in two-dimensional passive source localisation using time difference of arrival measurements," vol.12(3), pp.310-319, 5 2018. This study presents a new approach to sensor placement strategy in emitter localisation problems based on time difference of arrival measurements. The method addresses a flexible procedure which is capable of positioning the sensors in constrained environments or non-stationary situations where the positions of the sensors are restricted to certain parts of the space and/or need to be changed repeatedly. This method is sequential and has lower computation burden compared to other methods. The validity of the proposed algorithm is assessed by many different numerical scenarios and the results verify its proper operation.

Siavash Rajabi;Seyed Ali Ghorashi;Vahid Shah-Mansouri;Hamidreza Karami; "Device-to-device communications using EMTR technique," vol.12(3), pp.320-326, 5 2018. Device-to-device (D2D) communication is a promising 5G technology, which helps to increase spectrum efficiency and sum-rate, as well as to decrease experienced latency. The main challenges of D2D communication are power consumption of paired devices, channel estimation between device pairs, and interference management between devices that use the same time and frequency resources. The electromagnetic time reversal (EMTR) technique is used in D2D communications to focus the signal power in both time and space domains for power efficiency of end users, to simplify the structure of transceivers, to help channel estimation in a low complexity way, and to nullify the effect of interference. First, in the time domain, the effect of using EMTR technique is investigated and its performance is compared with a similar non-EMTR system. Then, EMTR technique is used in an OFDM-based system and its advantages in the context of signal to interference plus noise ratio (SINR) and sum-rate are presented analytically and through computer simulations. Simulation results show a significant gain in SINR, sum-rate and received signal power for the proposed EMTR-based D2D system.

Ehsan Tohidi;Mojtaba Radmard;Mohammad Nazari Majd;Hamid Behroozi;Mohammad Mahdi Nayebi; "Compressive sensing MTI processing in distributed MIMO radars," vol.12(3), pp.327-334, 5 2018. It is shown that the detection performance can be significantly improved using the recent technology of multiple-input multiple-output (MIMO) radar systems. This is a result of the spatial diversity in such systems due to the viewing of the target from different angles. On the other hand, the moving target indication (MTI) processing has long been known and applied in the traditional pulse radars to detect weak moving targets in the presence of strong clutter signals. The authors propose a procedure based on the compressive sensing idea, in order to apply the MTI processing in a MIMO radar with widely separated antennas. Although a clutter is included in the signal model and a different radar cross-section value for each transmitter-receiver pair is considered which makes the problem more complex, the complexity dimension is preserved as low as possible by converting the block sparse problem into a regular sparse problem.

Mohammad Reza Anbiyaei;Wei Liu;Des C. McLernon; "White noise reduction for wideband linear array signal processing," vol.12(3), pp.335-345, 5 2018. The performance of wideband array signal processing algorithms is dependent on the noise level in the system. A method is proposed for reducing the level of white noise in wideband linear arrays via a judiciously designed spatial transformation followed by a bank of highpass filters. A detailed analysis of the method and its effect on the spectrum of the signal and noise are presented. The reduced noise level leads to a higher signal-to-noise ratio for the system, which can have a significant beneficial effect on the performance of various beamforming methods and other array signal processing applications such as direction of arrival estimation. Here the authors focus on the beamforming problem and study the improved performance of two well-known beamformers, namely the reference signal based and the linearly constrained minimum variance beamformers. Both theoretical analysis and simulation results are provided.

Ghanbar Azarnia;Mohammad Ali Tinati;Tohid Yousefi Rezaii; "Cooperative and distributed algorithm for compressed sensing recovery in WSNs," vol.12(3), pp.346-357, 5 2018. Wireless sensor networks (WSNs) could benefit a lot from compressive sensing (CS). Inherent physical structure of sensors of WSNs (battery-powered devices) demands computational-efficient algorithms with no heavy burden on a small subset of the sensors, i.e. fusion sensors. This could be achieved by distributed algorithms in which computation is distributed among all sensor nodes. On this basis, in this study, the authors have proposed a distributed and cooperative sparse recovery algorithm in which each sensor decodes a sparse signal by running a recovery algorithm with the cooperation of its neighbours. The proposed algorithm has a general structure and can be adapted to many optimisation algorithms in the context of the CS. This algorithm is completely distributed and requires an acceptable computational complexity that is suitable for WSNs. A detailed proof of convergence behaviour of the proposed algorithm is also presented. The superiority of the proposed algorithm compared with similar methods in terms of recovery quality and convergence rate is confirmed through simulation.

Ying Lu;Yougen Xu;Yulin Huang;Zhiwen Liu; "Diversely polarised antenna-array-based narrowband/wideband beamforming via polarisational reconstruction matrix inversion," vol.12(3), pp.358-367, 5 2018. The problem of beamforming using diversely polarised antenna array is addressed in the study. With the attempt to avoid signal cancellation caused by very small snapshot number, and/or look direction error and/or polarisation mismatch, the polarisational reconstruction matrix inversion based beamformer is developed for both the narrowband and wideband cases. The major contribution is on the reconstruction of the narrowband case interference-plus-noise covariance matrix and wideband case focused interference-plus-noise covariance matrix, for the cases of both completely polarised and partially or randomly polarised interferences, by the rank-1 and rank-2 spatial spectrum techniques, respectively. The performance of the proposed beamformer, against small snapshot number, look direction error, polarisation mismatch, and focusing error, was evaluated by extensive simulations, and compared with the polarisational extensions of some existing popular polarisation insensitive beamformers, in terms of the output signal-to-interference-plus-noise ratio values and the experimental deviation of the signal-of-interest (SOI) estimate. The influences of direction difference between SOI and interference, degrees of polarisation of SOI and interference, and integrating range for reconstruction on beamforming performance are also studied.

Zhouwen Tan;Hongli Liu; "Adaptive decision directed impulse noise mitigate in power line communication," vol.12(3), pp.368-374, 5 2018. Impulse noise (IN) is the main cause of performance degradation in high-speed power line communication systems. Traditional methods mainly focus on manually setting a fixed blanking threshold to mitigate the corresponding IN. However, the fixed threshold cannot adapt to the time-varying IN efficiently. To solve this problem, an adaptive IN-mitigation system is proposed based on orthogonal frequency division multiplexing in a time-varying IN channel. The characteristics of IN are pre-evaluated by the method of moment estimation. Moreover, the adaptive threshold is efficiently solved in closed form according to the IN characteristics. In addition, an adaptive iterative IN-mitigation block is designed to leverage the performance of the receiver. For the number of iterations, a look-up table is constructed according to the IN characteristics. The experimental results show that the proposed method achieves performance balance in weak, moderate, and heavy IN environments simultaneously. It is noted that the bit error rate significantly decreases with an increment in $E_{rm b}/N_0$Eb/N0.

Xingbo Chen;Zhipeng Liu;Yiming Liu;Zhaowei Wang; "Energy leakage analysis of the radar and communication integrated waveform," vol.12(3), pp.375-382, 5 2018. When minimum shift keying (MSK)'s carrier is changed as the linear frequency modulation (LFM) signal, it is called as MSK-LFM for short. It is a new type of the radar-communication multifunctional integrated waveform. Under the assumption that the bandwidth of the radar receiver is equal to that of the LFM signal, the energy leakage of MSK-LFM is analysed. The trade-off between the number of modulated bits and the energy leakage is derived analytically. The derived equation demonstrates that the relationship between the radar detection performance and digital communication performance of MSK-LFM, which also gives the principle of designing the integrated waveform. To better ensure the radar detection performance, a method is proposed to reduce the energy leakage at the expense of sacrificing certain communication capacity.

IEEE Transactions on Geoscience and Remote Sensing - new TOC (2018 May 24) [Website]

* "[Front cover[Name:_blank]]," vol.56(6), pp.C1-C1, June 2018.* Presents the front cover for this issue of the publication.

* "IEEE Transactions on Geoscience and Remote Sensing publication information," vol.56(6), pp.C2-C2, June 2018.* Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

* "Table of contents," vol.56(6), pp.3005-3628, June 2018.* Presents the table of contents for this issue of the publication.

Øivind Kåre Kjerstad;Sveinung Løset;Roger Skjetne;Runa A. Skarbø; "An Ice-Drift Estimation Algorithm Using Radar and Ship Motion Measurements," vol.56(6), pp.3007-3019, June 2018. This paper presents a novel automatic real-time remote sensing algorithm that uses radar images and global positioning satellite system measurements to estimate the ice-drift velocity vector in a region around a free-floating and potentially moving vessel. It is motivated by the low image frequency of satellite systems together with the inconvenience of deploying and retrieving ice trackers (beacons) on the ice. The algorithm combines radar image processing with two Kalman filters to produce the estimated local drift vector decoupled from the ship motion. The proposed design is verified using a full-scale data set from an ice management operation north of Svalbard in 2015. It is found that the performance of the algorithm is comparable with that of trackers on the ice.

Bo Liu;Mohamed Mohandes;Hilal Nuha;Mohamed Deriche;Faramarz Fekri; "A Distributed Principal Component Analysis Compression for Smart Seismic Acquisition Networks," vol.56(6), pp.3020-3029, June 2018. This paper develops a new framework for data compression in seismic sensor networks by using the distributed principal component analysis (DPCA). The proposed DPCA scheme compresses all seismic traces in the network at the sensor level. First of all, the statistics of the seismic traces acquired at all sensors are represented by a mixture model of a number of probability density functions. Based on this mixture model, the DPCA finds the global PCs at the fusion center. These PCs are then sent back to all sensors so that each sensor projects its own traces over these PCs. This scheme does not require transmitting the original traces, here, leading to a low computational load and a high compression ratio, compared with compression obtained using the local PC analysis (LPCA). Furthermore, we develop an efficient communication solution for the DPCA implementation on practical sensor networks. Finally, the proposed scheme is evaluated using real and synthetic seismic data showing improved performance over the LPCA and the traditional 2-D discrete cosine transform (DCT-2-D) compression. Specifically, to preserve a given signal energy during the compression, the DPCA is shown to achieve a higher compression ratio than the LPCA and the DCT-2-D.

Daniele Marinelli;Claudia Paris;Lorenzo Bruzzone; "A Novel Approach to 3-D Change Detection in Multitemporal LiDAR Data Acquired in Forest Areas," vol.56(6), pp.3030-3046, June 2018. Light Detection and Ranging (LiDAR) data have been widely used to characterize the 3-D structure of the forest. However, their use in a multitemporal framework has been quite limited due to the relevant challenges introduced by the comparison of pairs of point clouds. Because of the irregular sampling of the laser scanner and the complex structure of forest areas, it is not possible to perform a point-to-point comparison between the two data. To overcome these challenges, a novel hierarchical approach to the detection of 3-D changes in forest areas is proposed. The method first detects the large changes (e.g., cut trees) by comparing the Canopy Height Models derived from the two LiDAR data. Then, according to an object-based change detection approach, it identifies the single-tree changes by monitoring both the treetop and the crown volume growth. The proposed approach can compare LiDAR data with significantly different pulse densities, thus allowing the use of many data available in real applications. Experimental results pointed out that the method can accurately detect large changes, exhibiting a low rate of false and missed alarms. Moreover, it can detect changes in terms of single-tree growth, which are consistent with the expected growth rates of the considered areas.

Teng-YU Ji;Naoto Yokoya;Xiao Xiang Zhu;Ting-Zhu Huang; "Nonlocal Tensor Completion for Multitemporal Remotely Sensed Images’ Inpainting," vol.56(6), pp.3047-3061, June 2018. Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. This paper aims at reconstructing the missing information by a nonlocal low-rank tensor completion method. First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then, low rankness of the identified fourth-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared with other patch-based methods such as the recently proposed patch matching-based multitemporal group sparse representation method.

Jian Kang;Yuanyuan Wang;Michael Schmitt;Xiao Xiang Zhu; "Object-Based Multipass InSAR via Robust Low-Rank Tensor Decomposition," vol.56(6), pp.3062-3077, June 2018. The most unique advantage of multipass synthetic aperture radar interferometry (InSAR) is the retrieval of long-term geophysical parameters, e.g., linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed by Kang, as an alternative to the typical single-pixel methods, e.g., persistent scatterer interferometry (PSI), or pixel-cluster-based methods, e.g., SqueeSAR. This enables the exploitation of inherent properties of InSAR phase stacks on an object level. As a follow-on, this paper investigates the inherent low rank property of such phase tensors and proposes a Robust Multipass InSAR technique via Object-based low rank tensor decomposition. We demonstrate that the filtered InSAR phase stacks can improve the accuracy of geophysical parameters estimated via conventional multipass InSAR techniques, e.g., PSI, by a factor of 10–30 in typical settings. The proposed method is particularly effective against outliers, such as pixels with unmodeled phases. These merits, in turn, can effectively reduce the number of images required for a reliable estimation. The promising performance of the proposed method is demonstrated using high-resolution TerraSAR-X image stacks.

Yuming Xiang;Feng Wang;Hongjian You; "OS-SIFT: A Robust SIFT-Like Algorithm for High-Resolution Optical-to-SAR Image Registration in Suburban Areas," vol.56(6), pp.3078-3090, June 2018. Although the scale-invariant feature transform (SIFT) algorithm has been successfully applied to both optical image registration and synthetic aperture radar (SAR) image registration, SIFT-like algorithms have failed to register high-resolution (HR) optical and SAR images due to large geometric differences and intensity differences. In this paper, to perform optical-to-SAR (OS) image registration, we proposed an advanced SIFT-like algorithm (OS-SIFT) that consists of three main modules: keypoint detection in two Harris scale spaces, orientation assignment and descriptor extraction, and keypoint matching. Considering the inherent properties of SAR images and optical images, the multiscale ratio of exponentially weighted averages and multiscale Sobel operators are used to calculate consistent gradients for the SAR images and optical images on the basis of which, as a result, two Harris scale spaces can be constructed. Keypoints are detected by finding the local maxima in the scale space followed by a localization refinement method based on the spatial relationship of the keypoints. Moreover, gradient location orientation histogram-like descriptors are extracted using multiple image patches to increase the distinctiveness. The experimental results on simulated images and several HR satellite images show that the proposed OS-SIFT algorithm gives a robust registration result for optical-to-SAR images and outperforms other state-of-the-art algorithms in terms of registration accuracy.

Yulei Wang;Li-Chien Lee;Bai Xue;Lin Wang;Meiping Song;Chunyan Yu;Sen Li;Chein-I Chang; "A Posteriori Hyperspectral Anomaly Detection for Unlabeled Classification," vol.56(6), pp.3091-3106, June 2018. Anomaly detection (AD) generally finds targets that are spectrally distinct from their surrounding neighborhoods but cannot discriminate its detected targets one from another. It cannot even perform classification because there is no prior knowledge about the data. This paper presents a new approach to AD, to be called a posteriori AD for unlabeled anomaly classification where a posteriori indicates that information obtained directly from processing data is used as new information for subsequent data processing. In particular, a posteriori AD uses a Gaussian filter to capture spatial correlation of detected anomalies as a posteriori information which is included as new information for further AD. In doing so, a posteriori AD develops an iterative version of AD, referred to as iterative anomaly detection (IAD), which implements AD by feeding back Gaussian-filtered AD maps in an iterative manner. It then uses an unsupervised target detection algorithm to identify spectrally distinct anomalies that can be used to specify particular anomaly classes. To terminate IAD, an automatic stopping rule is also derived. Finally, it uses identified distinct anomalies as desired target signatures to implement constrained energy minimization (CEM) to classify all detected anomalies into unlabeled classes. The experimental results show that a posteriori AD is indeed very effective in unlabeled anomaly classification.

Yakoub Bazi;Farid Melgani; "Convolutional SVM Networks for Object Detection in UAV Imagery," vol.56(6), pp.3107-3118, June 2018. Nowadays, unmanned aerial vehicles (UAVs) are viewed as effective acquisition platforms for several civilian applications. They can acquire images with an extremely high level of spatial detail compared to standard remote sensing platforms. However, these images are highly affected by illumination, rotation, and scale changes, which further increases the complexity of analysis compared to those obtained using standard remote sensing platforms. In this paper, we introduce a novel convolutional support vector machine (CSVM) network for the analysis of this type of imagery. Basically, the CSVM network is based on several alternating convolutional and reduction layers ended by a linear SVM classification layer. The convolutional layers in CSVM rely on a set of linear SVMs as filter banks for feature map generation. During the learning phase, the weights of the SVM filters are computed through a forward supervised learning strategy unlike the backpropagation algorithm widely used in standard convolutional neural networks (CNNs). This makes the proposed CSVM particularly suitable for detecting problems characterized by very limited training sample availability. The experiments carried out on two UAV data sets related to vehicles and solar-panel detection issues, with a 2-cm resolution, confirm the promising capability of the proposed CSVM network compared to recent state-of-the-art solutions based on pretrained CNNs.

Feng Qiu;Jing M. Chen;Weimin Ju;Jun Wang;Qian Zhang;Meihong Fang; "Improving the PROSPECT Model to Consider Anisotropic Scattering of Leaf Internal Materials and Its Use for Retrieving Leaf Biomass in Fresh Leaves," vol.56(6), pp.3119-3136, June 2018. The PROSPECT model has been widely used to estimate leaf biochemical constituents, but retrieval of leaf mass per area (LMA) in fresh leaves has proved to be difficult due to the predominant water absorption in the infrared spectral region. At wavelengths where water absorption is low, both LMA absorption and light scattering are relatively high. Therefore, the uncertainty in scattering simulation at these wavelengths will lead to a relatively large error in LMA estimation. In this paper, we introduce a wavelength-independent factor to represent the first-order effect of anisotropic scattering in the elementary layer in the modified model PROSPECT-g, aiming at appropriately simulating leaf optical properties in spectral regions with high scattering and thus reducing the uncertainty in LMA estimation. In order to avoid introducing a new variable to be retrieved in model inversion, this factor is an intermediate variable derived from measured near infrared region spectral data and other existing model parameters. Results show that about 30%–40% of the tested samples are well simulated using PROSPECT-5, while for the rest of the samples simulation is greatly improved with PROSPECT-g. Leaf reflectance and transmittance reconstructions using PROSPECT-g are improved, especially at wavelengths with high scattering such as 750–1400 and 1500–1850 nm. LMA retrieval is significantly improved, with the average root-mean-square error decreasing from 38.7 (PROSPECT-5) to 16.6 g/m2 (PROSPECT-g) for 628 leaves after considering anisotropic scattering in the elementary layer. Improvements are particularly noticeable for leaves with extremely high LMA contents.

Tai Qiao;Leung Tsang;Douglas Vandemark;Simon H. Yueh;Tien-Hao Liao;Frédéric Nouguier;Bertrand Chapron; "Sea Surface Radar Scattering at L-Band Based on Numerical Solution of Maxwell’s Equations in 3-D (NMM3D)," vol.56(6), pp.3137-3147, June 2018. Radar scattering from ocean surfaces is investigated by 3-D numerical solution of Maxwell’s equations [numerical Maxwell’s model in 3-D (NMM3D)] using the ocean surface profiles stochastically generated from a 3-D Durden–Vesecky ocean spectrum. The surface integral equations (SIEs) are formulated for dielectric surfaces using Green’s functions of the air and the ocean permittivities with the surface tangential electric and magnetic fields as the unknowns. In solving the SIEs using the method of moment, a fast matrix solver of the sparse matrix canonical grid is used in conjunction with Rao–Wilton–Glisson basis functions. The computation has been implemented on a high-performance parallel computing cluster for problems with up to six million surface unknowns. Unlike the two-scale model (TSM) approximation, NMM3D does not require division of the surface spectrum into large- and small-scale ocean waves. The results of backscattering simulations are compared to Aquarius satellite radar measurements for wind speeds of 5, 8, and 10 m/s and for incidence angles of 29°, 39°, and 46°. The results show that NMM3D ocean backscattering solutions at L-band are in good agreement with Aquarius satellite radar data for co-polarized VV, HH, and cross-polarized VH returns as well as for the VV/HH ratio. The azimuthal dependence of L-band backscatter is also assessed. Finally, NMM3D results are compared to TSM solutions and are shown to lie close to Aquarius data in observed VV/HH ratio, and their azimuthal dependencies.

Sergei Rudenko;Mathis Bloßfeld;Horst Müller;Denise Dettmering;Detlef Angermann;Manuela Seitz; "Evaluation of DTRF2014, ITRF2014, and JTRF2014 by Precise Orbit Determination of SLR Satellites," vol.56(6), pp.3148-3158, June 2018. In 2016, three new realizations of the International Terrestrial Reference System (ITRS), namely, DTRF2014, ITRF2014, and JTRF2014, have been released. In this paper, we evaluate these ITRS realizations for precise orbit determination of ten high and low Earth orbiting geodetic satellites using satellite laser ranging (SLR) observations. We show the reduction of observation residuals and estimated range biases, when using these new ITRS realizations, as compared with the previous ITRS realization for SLR stations—SLRF2008. Thus, the mean SLR root-mean-square (RMS) fits reduce (improve), on average over all satellites tested, by 3%, 3.6%, 8.1%, and 7.7% at 1993.0–2015.0, when using ITRF2014, DTRF2014, and DTRF2014 with non-tidal loading (NTL), and JTRF2014 realizations, respectively. The improvement of the RMS fits is even larger at 2015.0–2017.0: 14% and 15.5% using ITRF2014 and DTRF2014, respectively. For the altimetry satellite Jason-2, we found improvements in the RMS and mean of the sea surface height crossover differences with the new ITRS realizations, as compared with SLRF2008. We show that JTRF2014, after an editing done for SLR stations Conception and Zimmerwald, and DTRF2014 with NTL corrections result in the smallest RMS and absolute mean fits of SLR observations indicating the best performance among the ITRS realizations tested, while using SLRF2008 and ITRF2014 causes a 0.2–0.3 mm/y trend in the mean of SLR fits at 2001.0–2017.0.

Weilin Huang;Ru-Shan Wu;Runqiu Wang; "Damped Dreamlet Representation for Exploration Seismic Data Interpolation and Denoising," vol.56(6), pp.3159-3172, June 2018. The dreamlet (drumbeat-beamlet) transform can provide us an efficient method to represent physical wavefield, because the dreamlet basis satisfies automatically the wave equation, which is a distinctive feature different from mathematical basis, such as Fourier and curvelet. It can obtain an estimation of true signal from the observed noisy data by abandoning those insignificant components in the dreamlet domain. However, we have found that a more accurate estimation can be achieved by a damped version of the dreamlet representation. We have theoretically derived the damped dreamlet representation and given its geometric interpretation and analysis. Two applications of the proposed method have been explored in this paper: seismic random noise suppression and seismic data interpolation. Various examples demonstrate that the damped dreamlet representation-based technique has a superior performance compared with the mathematical-basis-based representation and rank-reduction-based techniques.

Weiwei Song;Shutao Li;Leyuan Fang;Ting Lu; "Hyperspectral Image Classification With Deep Feature Fusion Network," vol.56(6), pp.3173-3184, June 2018. Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved good performance. In general, deep models adopt a large number of hierarchical layers to extract features. However, excessively increasing network depth will result in some negative effects (e.g., overfitting, gradient vanishing, and accuracy degrading) for conventional convolutional neural networks. In addition, the previous networks used in HSI classification do not consider the strong complementary yet correlated information among different hierarchical layers. To address the above two issues, a deep feature fusion network (DFFN) is proposed for HSI classification. On the one hand, the residual learning is introduced to optimize several convolutional layers as the identity mapping, which can ease the training of deep network and benefit from increasing depth. As a result, we can build a very deep network to extract more discriminative features of HSIs. On the other hand, the proposed DFFN model fuses the outputs of different hierarchical layers, which can further improve the classification accuracy. Experimental results on three real HSIs demonstrate that the proposed method outperforms other competitive classifiers.

Weiwei Sun;Qian Du; "Graph-Regularized Fast and Robust Principal Component Analysis for Hyperspectral Band Selection," vol.56(6), pp.3185-3195, June 2018. A fast and robust principal component analysis on Laplacian graph (FRPCALG) method is proposed to select bands of hyperspectral imagery (HSI). The FRPCALG assumes that a clean band matrix lies in a unified manifold subspace with low-rank and clustering properties, whereas sparse noise does not lie in the same subspace. It estimates the clean low-rank approximation of the original HSI band matrix while uncovering the clustering structure of all bands. Specifically, a structured random projection is adopted to reduce the high spatial dimensionality of the original data for computational cost saving, and then a Laplacian graph (LG) term is regularized into the regular robust principal component analysis (RPCA) to formulate the FRPCALG model for the submatrix of bands to be selected. The RPCA term ensures the clean and low-rank approximation of original data, and the LG term guarantees the clustering quality of a low-rank matrix in the low-dimensional manifold subspace. The alternating direction method of multipliers’ algorithm is utilized to optimize the convex program of the FRPCALG. The K-means algorithm is to group all columns of submatrix into clusters, and corresponding bands closest to their cluster centroids finally constitute the desired band subset. Experimental results show that FRPCALG outperforms state-of-the-art methods with lower computational cost. A moderate regularization parameter <inline-formula> <tex-math notation="LaTeX">$lambda $ </tex-math></inline-formula> and a small <inline-formula> <tex-math notation="LaTeX">$mu $ </tex-math></inline-formula> could guarantee satisfying the classification accuracy of FRPCALG, and a small projected dimension greatly reduces the computational cost and does not affect the classification performance. Therefore, the FRPCALG can be an alternative method for hyperspectral band selection.

Ronghai Hu;Guangjian Yan;Françoise Nerry;Yunshu Liu;Yumeng Jiang;Shuren Wang;Yiming Chen;Xihan Mu;Wuming Zhang;Donghui Xie; "Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index," vol.56(6), pp.3196-3209, June 2018. The airborne laser scanner (ALS) provides great potential for mapping the leaf area index (LAI) at the landscape scale using grid cell statistics, while its application is restricted by the lack of clumping information, which has been an unsolved issue highlighted for a long time. ALS generally provides an effective LAI because its footprint is too large to capture small gaps to apply traditional ground-based clumping correction methods. Here, we present a grid cell method based on path length distribution model to calculate the clumping-corrected LAI using ALS data without the requirement of additional field measurements. We separated the within- and between-crown areas to consider between-crown clumping, and used the path length distribution as estimated by local canopy height distribution to consider 3-D foliage profile and within-crown clumping. The path length distribution model takes advantage of the 3-D information rather than the gap size distribution, thus avoiding the limitation of large ALS footprint. With the 0.4-m-footprint ALS data, the results are generally promising and a multilevel clumping analysis is consistent with landscape flown. The ALS LAIs of different resolutions are consistent, with a difference of less than 5% from 5- to 250-m resolutions. Due to its consistency and simple configuration, the method provides an opportunity to map the clumping-corrected LAI operationally and strengthens the ability of airborne lidar to monitor vegetation change and validate the satellite product. This grid cell method based on path length distribution is worth further testing and application using more recent laser technology.

Liu Mei;Wang Pengfei;Wang Zhigui;Li Chenlei; "Multidimensional Motion Parameters’ Estimation for Multi-SAR System Using the Cubic Phase Function," vol.56(6), pp.3210-3221, June 2018. In this paper, the motion parameters’ estimation problem in synthetic aperture radar (SAR) imaging is extended to the multidimensional case, and the azimuth signal of a moving target with velocities and accelerations in three directions is modeled as a polynomial-phase signal (PPS). To estimate the multidimensional motion parameters of the moving target from the azimuth PPS, a multi-SAR system is required. On this basis, a new method based on the cubic phase function (CPF) is proposed to estimate the Doppler phase coefficients (DPCs) of the PPS. And, all the first- and second-order motion terms can be derived from the DPC. Experimental results have shown that, using the proposed scheme, the motion parameters can be accurately estimated in high signal-to-noise ratio scenarios.

Asko Ristolainen;Kaia Kalev;Jeffrey Andrew Tuhtan;Alar Kuusik;Maarja Kruusmaa; "Hydromorphological Classification Using Synchronous Pressure and Inertial Sensing," vol.56(6), pp.3222-3232, June 2018. Classification of river morphology is often based on hydromorphological units (HMUs) identified from field measurements. Established survey methods rely on expert judgment or collection of field point measurements. When used for HMU classification, these methods can suffer from high errors due to the variations in the sampling environment, causing low repeatability. In order to expedite field data collection and increase HMU classification accuracy, we propose a multisensory device, the hydromast. Each hydromast provides a new source of data to classify HMUs. The modules are inexpensive and highly portable, consisting of a synchronous array of commodity pressure and inertial sensors. Rapid, local changes in the flow field are recorded with absolute and differential pressure sensors. At the same time, slower depth-integrated flow signals are obtained from a small damped cylindrical mast, driven by vortex-induced vibrations. In contrast to existing passive flow measurement technologies, the hydromast uses fluid–body interactions to provide flow measurements. This allows for minimal signal processing and simple feature extraction. An array of three hydromasts was used to collect ten samples in three river HMUs with shallow depths and highly turbulent flows with smooth and rough beds. We investigated classification accuracy using single, dual, and triple hydromast arrays with pressure, inertial, and combined features using linear regression, a genetic algorithm, and a neural network. Although limited in scope, the set of spot measurements covering three HMUs showed that a single multimodal sensor could deliver an overall classification accuracy of 89% of the HMUs, and an increase of up to 99% was achieved using a multimodal triple hydromast array. These preliminary results show promise in using hydromasts for rapid and robust HMU classification, providing a new way to collect and assess river survey data.

Binge Cui;Xiaoyun Xie;Xiudan Ma;Guangbo Ren;Yi Ma; "Superpixel-Based Extended Random Walker for Hyperspectral Image Classification," vol.56(6), pp.3233-3243, June 2018. In this paper, a novel SuperPixel-based Extended Random Walker (SPERW) classification method for hyperspectral images is proposed that consists of three main steps. First, a multiscale segmentation algorithm is adopted to generate many superpixels, each of which represents a homogeneous region of adaptive shape and size. Then, a new weighted graph is constructed based on the superpixels in which the nodes correspond to the superpixels and the edges correspond to the links connecting two adjacent superpixels. Each edge has a weight that defines the similarity between the two superpixels. Second, a widely used pixelwise classifier, i.e., the support vector machine, is adopted to obtain classification probability maps for a hyperspectral image, which are then used to approximate the prior probabilities of the superpixels. Finally, the obtained prior probability maps of the superpixels are optimized by using the Extended Random Walker (ERW) algorithm, which encodes the spatial information both among and within the superpixels of the hyperspectral image in a weighted graph. Compared with the spectrum of a single pixel, the spectrum of a superpixel is more stable and less affected by noise; therefore, superpixels are more appropriate for adoption as the basic elements in the hyperspectral image classification. Because the spectral correlation between pixels within the same superpixel and the spatial correlation among adjacent superpixels are both well considered in the ERW-based global optimization framework, the proposed method shows high classification accuracy on four widely used real hyperspectral data sets even when the number of training samples is relatively small.

Lionel Zawadzki;Michaël Ablain;Loren Carrere;Richard D. Ray;Nikita P. Zelensky;Florent Lyard;Amandine Guillot;Nicolas Picot; "Investigating the 59-Day Error Signal in the Mean Sea Level Derived From TOPEX/Poseidon, Jason-1, and Jason-2 Data With FES and GOT Ocean Tide Models," vol.56(6), pp.3244-3255, June 2018. Since the beginning of the altimeter mission TOPEX/Poseidon (T/P), followed by Jason-1 and Jason-2 on similar orbits, and many other missions on different orbits (ERS, EnviSat, etc.), mean sea level (MSL) products became essential for the comprehension of global ocean circulation. Since early in the T/P mission, a suspicious signal, having a period of near 59 days and amplitude of roughly 5 mm, was apparent in the Global MSL record. Compared with the 4–5-mm amplitude of the annual signal, the 59-day signal has understandably attracted attention. Moreover, the same signal has been subsequently detected in Jason-1 and later in Jason-2 MSLs. In 2010, the Ocean Surface Topography Science Team (OSTST) concluded this signal as the aliasing of a higher frequency error inherited from the tide model correction: the semi-diurnal wave S2. The source of this error was mainly attributed to T/P measurements, which were assimilated in ocean tide models. When these models are used in the computation of T/P MSL, most of the error cancels. However, this error is communicated to Jason-1 and Jason-2 MSLs. In order to gather and publish the OSTST analyses on this matter, this paper first attempts to list the myriad possibilities for the puzzling 59-day error in MSL. Then, this paper goes deeper into the description of the main contributor to this list: the tide models error. Indeed, since 2010, considerable efforts have been undertaken within the ocean tide community in order to correct ocean tide S2-waves from this error, particularly in the Goddard Ocean Tide (GOT) and finite element solution (FES) latest versions. Comparing several GOT and FES versions and a pure hydrodynamic tide model, this paper assesses, quantifies, and describes a reduction of the MSL 59-day error thanks to the latest releases. These analyses also confirm that a large part of this error has its origins in the T/P mission and has contaminated ocean tide solutions and Jason-1 and Jason-2 MSLs. - hey also suggest that ocean tide is not the only possible vector. Jason-1 and Jason-2 MSLs contain additional 59-day error—though to a lesser extent—that may either come from the measurements themselves or from another vector.

D. Josset;J. Pelon;N. Pascal;Y. Hu;W. Hou; "On the Use of CALIPSO Land Surface Returns to Retrieve Aerosol and Cloud Optical Depths," vol.56(6), pp.3256-3264, June 2018. The quantification of aerosol and cloud radiative properties, optical depth (OD), and phase function is of high importance to quantify the human impact on climate. Several approaches now exist based on both active (lidar) and passive (spectroradiometers) sensors. However, passive space observations over land are hindered by the important contribution of the surface to the total reflectance. Retrievals of OD from backscatter lidars do not face this issue but are usually based on the use of an a priori value of the so-called lidar ratio, which may lead to a significant uncertainty. The objective of this paper is to analyze a possible path for the space borne backscatter lidar onboard the Cloud Aerosol Lidar Pathfinder Observations satellite to overcome those issues. We will discuss the space-borne retrievals of ODs based on the land surface returns, either in combination with the Moderate Resolution Imaging Spectroradiometer or as a stand-alone lidar method. Analyses will be presented for a few cases on different surface types. The different error sources are discussed and further solutions to reduce them are explored. We show that the surface types have different polarization and multispectral properties, which can open new research areas based on space lidars. Using such an approach, we show that a retrieval technique based on the use of lidar land surface returns can be used to directly retrieve OD of aerosols and semitransparent cloud.

Shaoquan Zhang;Jun Li;Heng-Chao Li;Chengzhi Deng;Antonio Plaza; "Spectral–Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing," vol.56(6), pp.3265-3276, June 2018. Spectral unmixing aims at estimating the fractional abundances of a set of pure spectral materials (endmembers) in each pixel of a hyperspectral image. The wide availability of large spectral libraries has fostered the role of sparse regression techniques in the task of characterizing mixed pixels in remotely sensed hyperspectral images. A general solution for sparse unmixing methods consists of using the <inline-formula> <tex-math notation="LaTeX">$ell _{1}$ </tex-math></inline-formula> regularizer to control the sparsity, resulting in a very promising performance but also suffering from sensitivity to large and small sparse coefficients. A recent trend to address this issue is to introduce weighting factors to penalize the nonzero coefficients in the unmixing solution. While most methods for this purpose focus on analyzing the hyperspectral data by considering the pixels as independent entities, it is known that there exists a strong spatial correlation among features in hyperspectral images. This information can be naturally exploited in order to improve the representation of pixels in the scene. In order to take advantage of the spatial information for hyperspectral unmixing, in this paper, we develop a new spectral–spatial weighted sparse unmixing (S2WSU) framework, which uses both spectral and spatial weighting factors, further imposing sparsity on the solution. Our experimental results, conducted using both simulated and real hyperspectral data sets, illustrate the good potential of the proposed S2WSU, which can greatly improve the abundance estimation results when compared with other advanced spectral unmixing methods.

Devis Tuia;Michele Volpi;Gabriele Moser; "Decision Fusion With Multiple Spatial Supports by Conditional Random Fields," vol.56(6), pp.3277-3289, June 2018. Classification of remotely sensed images into land cover or land use is highly dependent on geographical information at least at two levels. First, land cover classes are observed in a spatially smooth domain separated by sharp region boundaries. Second, land classes and observation scale are also tightly intertwined: they tend to be consistent within areas of homogeneous appearance, or regions, in the sense that all pixels within a roof should be classified as roof, independently on the spatial support used for the classification. In this paper, we follow these two observations and encode them as priors in an energy minimization framework based on conditional random fields (CRFs), where classification results obtained at pixel and region levels are probabilistically fused. The aim is to enforce the final maps to be consistent not only in their own spatial supports (pixel and region) but also across supports, i.e., by getting the predictions on the pixel lattice and on the set of regions to agree. To this end, we define an energy function with three terms: 1) a data term for the individual elements in each support (support-specific nodes); 2) spatial regularization terms in a neighborhood for each of the supports (support-specific edges); and 3) a regularization term between individual pixels and the region containing each of them (intersupports edges). We utilize these priors in a unified energy minimization problem that can be optimized by standard solvers. The proposed 2L↯CRF model consists of a CRF defined over a bipartite graph, i.e., two interconnected layers within a single graph accounting for interlattice connections. 2L↯CRF is tested on two very high-resolution data sets involving submetric satellite and subdecimeter aerial data. In all cases, 2L↯CRF improves the result obtained by the independent base model (either random forests or convolutional neural netw- rks) and by standard CRF models enforcing smoothness in the spatial domain.

Donato Amitrano;Gerardo Di Martino;Antonio Iodice;Daniele Riccio;Giuseppe Ruello; "Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images," vol.56(6), pp.3290-3299, June 2018. We present a new methodology for rapid flood mapping exploiting Sentinel-1 synthetic aperture radar data. In particular, we propose the usage of ground range detected (GRD) images, i.e., preprocessed products made available by the European Space Agency, which can be quickly treated for information extraction through simple and poorly demanding algorithms. The proposed framework is based on two processing levels providing event maps with increasing resolution. The first level exploits classic co-occurrence texture measures combined with amplitude information in a fuzzy classification system avoiding the critical step of thresholding. The second level consists of a change-detection approach applied to the full resolution GRD product. The discussion is supported by several experiments demonstrating the potentiality of the proposed methodology, which is particularly oriented toward the end-user community.

Ofir Lindenbaum;Yuri Bregman;Neta Rabin;Amir Averbuch; "Multiview Kernels for Low-Dimensional Modeling of Seismic Events," vol.56(6), pp.3300-3310, June 2018. The problem of learning from seismic recordings has been studied for years. There is a growing interest of developing automatic mechanisms for identifying the properties of a seismic event. One main motivation is the ability to have a reliable identification of man-made explosions. The availability of multiple high-dimensional observations has increased the use of machine learning techniques in a variety of fields. In this paper, we propose to use a kernel-fusion-based dimensionality reduction framework for generating meaningful seismic representations from raw data. The proposed method is tested on 2023 events that were recorded in Israel and Jordan. The method achieves promising results in the classification of event type as well as the estimation of the event location. The proposed fusion and dimensionality reduction tools may be applied to other types of geophysical data.

Yan Huang;Guisheng Liao;Jingwei Xu;Jie Li; "Narrowband RFI Suppression for SAR System via Efficient Parameter-Free Decomposition Algorithm," vol.56(6), pp.3311-3322, June 2018. Synthetic aperture radar (SAR), as a wideband radar system, is easy to be interfered by radio frequency systems, such as the radio, television, and cellular works. Since the narrowband radio frequency interference (RFI) has a relatively fixed frequency during the synthetic aperture time, it is removed as a low-rank term of the received signal in recent research. In this paper, we employ a novel “low-rank + sparse” decomposition model to extract the low-rank RFI and protect the strong scatterers of a useful signal, which is explicit and more efficient than the previous augmented Lagrange function model. Because the radar signal is complex, we exploit soft thresholding instead of hard thresholding in the Go Decomposition algorithm, which is defined as the revised traditional decomposition (RTD) method. Soft thresholding can recover the phase term correctly for a further focused image. Both the previous augmented Lagrange method and the proposed RTD method need to search the values of user parameters with high computational complexity. In order to eliminate the bother of tuning user parameters, a parameter-free decomposition (PFD) method is proposed to adaptively estimate the user parameters. Also, by considering the property of the useful signal, the PFD method protects the useful signal with adaptive thresholds for each snapshot. It has a better performance for RFI suppression, but costs slightly more computational time compared with the RTD method. The real SAR data and the measured RFI are provided to demonstrate the correctness of the proposed methods.

Chengxin Zhang;Cheng Liu;Yang Wang;Fuqi Si;Haijin Zhou;Minjie Zhao;Wenjing Su;Wenqiang Zhang;Ka Lok Chan;Xiong Liu;Pinhua Xie;Jianguo Liu;Thomas Wagner; "Preflight Evaluation of the Performance of the Chinese Environmental Trace Gas Monitoring Instrument (EMI) by Spectral Analyses of Nitrogen Dioxide," vol.56(6), pp.3323-3332, June 2018. The Environmental trace gas Monitoring Instrument (EMI) onboard the Chinese high-resolution remote sensing satellite GaoFen-5 is an ultraviolet–visible imaging spectrometer, aiming to quantify the global distribution of tropospheric and stratospheric trace gases and planned to be launched in spring 2018. The preflight calibration phase is essential to characterize the properties and performance of the EMI in order to provide information for data processing and trace gas retrievals. In this paper, we present the first EMI measurement of nitrogen dioxide (NO2) from a gas absorption cell using scattered sunlight as the light source by the differential optical absorption spectroscopy technique. The retrieved NO2 column densities in the UV and Vis wavelength ranges are consistent with the column density in the gas cell calculated from the NO2 mixing ratio and the length of the gas cell. Furthermore, the differences of the retrieved NO2 column densities among the adjoining spatial rows of the detector are less than 3%. This variation is similar to the well-known “stripes-pattern” of the Ozone Monitoring Instrument and is probably caused by remaining systematic effects like a nonperfect description of the individual instrument functions. Finally, the signal-to-noise ratios of EMI in-orbit measurements of NO2 are estimated on the basis of on-ground scattered sunlight measurements and radiative transfer model simulations. Based on our results, we conclude that the EMI is capable of measuring the global distribution of the NO2 column with the retrieval precision and accuracy better than 3% for the tested wavelength ranges and viewing angles.

Heather Lawrence;Niels Bormann;Alan J. Geer;Qifeng Lu;Stephen J. English; "Evaluation and Assimilation of the Microwave Sounder MWHS-2 Onboard FY-3C in the ECMWF Numerical Weather Prediction System," vol.56(6), pp.3333-3349, June 2018. This paper presents an evaluation of the quality of data from the MicroWave Humidity Sounder-2, flown on the FY-3C polar orbiting satellite, and first results of trials assimilating the data in all-sky conditions in the European Centre for Medium Range Weather Forecasts’ assimilation system. This instrument combines traditional microwave humidity sounding capabilities at 183 GHz with new channels at 118 GHz, which have never before been available from space. The aim of this paper is twofold: to contribute to the calibration/validation of this satellite by evaluating the data and to test for the first time the impact of assimilating 118-GHz sounding channels in a global numerical weather prediction system. MWHS-2 was first evaluated by comparing observation minus short-range forecast statistics to similar instruments, which indicated that the quality was generally similar. Adding the 118- and 183-GHz channels to the full operational observing system led to small improvements in the short-range (~12 h) forecast accuracy for both sets of channels, and improvements in the 2–4-day wind forecast accuracy for the 183-GHz channels. Short-range forecasts were improved for global humidity in particular when assimilating the 183-GHz channels (0.5% improvement) and for cloud and low-level wind in the Southern Hemisphere extra-tropics when assimilating the 118-GHz channels (by, respectively, 0.5% and 0.2%). In the absence of other atmospheric observations, assimilating the 118-GHz channels improved the global forecast accuracy, but the overall impact was less than for the 183-GHz channels, or for equivalent Advanced Microwave Sounding Unit-A channels.

Deepak Gopalakrishnan;Anantharaman Chandrasekar; "On the Improved Predictive Skill of WRF Model With Regional 4DVar Initialization: A Study With North Indian Ocean Tropical Cyclones," vol.56(6), pp.3350-3357, June 2018. The predictive skill of the weather research and forecasting model is examined, and the improved performance with the 4-D variational (4DVar) data assimilation scheme over the 3-D variational (3DVar) scheme is quantified for the simulation of four tropical cyclones over the Indian region by generating a large number of analysis/forecast samples. Satellite radiance, satellite-derived winds, and conventional observations are assimilated cyclically for both 3DVar and 4DVar experiments at an interval of 6 h. Each of the analyses is then subjected to a short-range free forecast, lasting 48 h. The analysis fields are found to capture the features of all the cyclones more realistically when the model is initialized with the 4DVar assimilation. Free forecasts from the 4DVar analysis fields are superior in simulating the intensity and position of the storm for most of the times. Rainfall simulation also shows marked improvement in terms of the equitable threat score for the 4DVar run. Furthermore, in the case of cyclone Phailin, it is noted that the 4DVar run could successfully capture the rapid intensification phase. On an average, the simulation of intensity and position has shown an improvement of 2%–43% and 22%–57%, respectively, by the 4DVar run at different forecast lead times.

Chongyue Zhao;Xinbo Gao;William J. Emery;Ying Wang;Jie Li; "An Integrated Spatio-Spectral–Temporal Sparse Representation Method for Fusing Remote-Sensing Images With Different Resolutions," vol.56(6), pp.3358-3370, June 2018. Different spectral, spatial, and temporal features have been widely used in the remote-sensing image analysis. The further development of multiple sensor remote-sensing technologies has made it necessary to explore new methods of remote-sensing image fusion using different optical image data sets which provide complementary image properties and a tradeoff among spatial, spectral, and temporal resolutions. However, due to problems in assessing correlations between different types of satellite data with different resolutions, a few efforts have been made to explore spatio-spectral–temporal features. For this purpose, we propose a novel sparse representation model to generate synthesized frequent high-spectral and high-spatial resolution data by blending multiple types: spatio-temporal data fusion, spectral–temporal data fusion, spatio-spectral data fusion, and spatio-spectral–temporal data fusion. The proposed method exploits high-spectral correlation across spectral domains and high self-similarity across spatial domains to learn the spatio-spectral fusion basis. Then, it associates temporal changes using a local constraint sparse representation. The integrated spatio-spectral–temporal sparse representation model based on the learned spectral–spatial and temporal change features strengthens the model’s ability to provide high-resolution data needed to address demanding work in real-world applications. Finally, the proposed method is not restricted to a certain type of data, but it can associate any type of remote-sensing data and be applied to dynamic changes in heterogeneous landscapes. The experimental results illustrate the effectiveness and efficiency of the proposed method.

Adugna G. Mullissa;Daniele Perissin;Valentyn A. Tolpekin;Alfred Stein; "Polarimetry-Based Distributed Scatterer Processing Method for PSI Applications," vol.56(6), pp.3371-3382, June 2018. Permanent scatterer interferometry is a multitemporal interferometric synthetic aperture radar technique that produces high-accuracy ground deformation measurement. A high density of permanent scatterer (PS) is required to provide accurate results. In natural environments with low PS density, distributed scatterers (DSs) could serve as additional coherent observations. This paper introduces a polarimetric scattering property-based adaptive filtering method that preserves PS candidates and filters DS candidates. To further increase the coherence estimate of DS candidates, the technique includes a complex coherence decomposition that adaptively selects the most stable scattering mechanisms, thus improving pixel coherence estimation. The proposed method was evaluated on 11 quad-polarized ALOS PALSAR images and 21 dual-polarized Sentinel-1 images acquired over San Fernando Valley, CA, USA, and Groningen, The Netherlands, respectively. The application of this method increased the number of coherent pixels by almost a factor of eight compared with a single-polarization channel. This paper concludes that a coherence estimate can be significantly improved by applying scattering property-based adaptive filtering and coherence matrix decomposition and accurate displacement measurements can be achieved.

Junhao Xie;Guowei Yao;Minglei Sun;Zhenyuan Ji; "Measuring Ocean Surface Wind Field Using Shipborne High-Frequency Surface Wave Radar," vol.56(6), pp.3383-3397, June 2018. Extraction of ocean surface wind field from data collected by shipborne high-frequency surface wave radar (HFSWR) is an ongoing challenge because of the inherent directional ambiguity and the effect of complicated platform motion on radar Doppler spectra. Here, a method for extracting the wind direction and speed from the spreading first-order radar Doppler spectra is first presented. First, the mathematical model of the wind direction versus a variable spreading parameter is developed. Moreover, based on the spreading characteristic of the first-order spectra, an approach for simultaneously determining the unambiguous wind direction and the unique spreading parameter with a single receiving antenna is presented. Furthermore, the relationship between the wind speed and the spreading parameter is derived on the basis of the relationship between the drag coefficient and the spreading parameter, and the wind speed can be determined. Therefore, the wind field of ocean area covered by shipborne HFSWR can be measured by sequentially exploiting the presented method, which is more beneficial for shipborne HFSWR because of smaller installation space and less cost. Simulation results and discussions of basic applications show the feasibility of wind field measurement in shipborne HFSWR. Experimental results validate the presented method and evaluate the detection accuracy and distance limit. The range for wind field measurement is up to 120 km, which is the range for which the signal-to-noise ratio typically exceeds about 11 dB in the relevant first-order portions of the backscatter spectra. Comparisons between the radar-measured and forecasting or buoy-measured results show good agreement.

Xiren Zhou;Huanhuan Chen;Jinlong Li; "An Automatic GPR B-Scan Image Interpreting Model," vol.56(6), pp.3398-3412, June 2018. Ground-penetrating radar (GPR) has been widely used as a nondestructive tool for the investigation of the subsurface, but it is challenging to automatically process the generated GPR B-scan images. In this paper, an automatic GPR B-scan image interpreting model is proposed to interpret GPR B-scan images and estimate buried pipes, which consists of the preprocessing method, the open-scan clustering algorithm (OSCA), the parabolic fitting-based judgment (PFJ) method, and the restricted algebraic-distance-based fitting (RADF) algorithm. First, a thresholding method based on the gradient information transforms the B-scan image to the binary image, and the opening and closing operations remove discrete noisy points. Then, OSCA scans the preprocessed binary image progressively to identify the point clusters1 with downward-opening signatures, and PFJ further validates whether the point clusters with downward-opening signatures are hyperbolic. By utilizing OSCA and PFJ, point clusters with hyperbolic signatures could be classified and segmented from other regions even if there are some connections and intersections between them. Finally, the validated point clusters are fitted into the lower parts of hyperbolas by RADF that solves fitting problems with additional constraints related to the hyperbolic central axis. By integrating these methods, the proposed model is able to extract information from GPR B-scan images automatically and efficiently. The experiments on simulated and real-world data sets demonstrate the effectiveness of the proposed model.

A point cluster is a collection of points with the same class identification.

Akanksha Garg;Dharmendra Singh; "Development of an Efficient Contextual Algorithm for Discrimination of Tall Vegetation and Urban for PALSAR Data," vol.56(6), pp.3413-3420, June 2018. Fully polarimetric synthetic aperture radar based land cover classification has been intensively investigated for past several decades, but it is still a challenging task to segregate tall vegetation and urban because scattering mechanism involved for both the classes is not sufficient to get the proper threshold in order to differentiate them. Therefore, there is a need to develop such a technique that has the capability to classify these classes with significantly better accuracy. Textural information of an image is known to be an alternate source of extracting useful information of targets. While dealing with natural targets, such as tall vegetation, characteristic of textural feature, i.e., roughness, may be an important parameter which could identify these targets, since both the classes possess different types of roughness. Henceforth, commonly used texture features, i.e., fractal dimension, lacunarity, Moran’s I, entropy, and correlation were critically analyzed and realized that these features are still lacking in the concerned segregation, because generally they are pixel-based. Consequently, neighboring pixels are taken into account and an approach has been developed by considering the randomness response (or manner of distribution of scatterers) based on relative similarity of total backscattering power of neighboring pixels by proposing a similarity entropy feature. An optimized threshold method is also developed by means of the contextual thresholding in order to provide a proper decision boundary between the two classes. The proposed approach is successfully tested and validated on different Phased Array type L-band Synthetic Aperture Radar data with sensitivity of tall vegetation and urban as 0.93 and 0.932, respectively.

Grant Matthews; "Signal Processing Enhancements to Improve Instantaneous Accuracy of a Scanning Bolometer: Application to MERBE," vol.56(6), pp.3421-3431, June 2018. Cloud radiative forcing climate signals cannot be detected and proved sufficiently by the existing space-based Earth Radiation Budget (ERB) measurements due to the insufficient instrument calibration accuracy, relative to the sizes of mere decadal-scale trends. This paper, therefore, introduces a new project called the Moon and ERB Experiment (MERBE). Its methodology is for all earth observations using broadband thermal detectors, such as bolometers, to adhere toward more traceable calibration standards based on scans of our moon. This traceability limits instrument-dependent biases and spurious drifts for the past decades of existing earth data measurements as well as future measurements. The goal of the MERBE project is to substantially increase existing satellite climate data accuracy; therefore, a reexamination of all aspects of space-based ERB device calibration was also warranted. This paper concentrates on the improvement of ERB data quality from the level of detector voltages and onward. A component model of bolometer thermal and electronic time response and offset effects is used to design an improved inversion filter, which deconvolves path direction dependence of a scanning thermal detector. For every ERB instrument on the TRMM, Terra, Aqua, and SNPP satellites, instantaneous error reductions are achieved in all recovered MERBE radiance measurements. This also will allow for more accurate results from ERB device lunar scans based on improved telescope field-of-view mapping, as presented in other MERBE work.

Amir Hossein Zaji;Hossein Bonakdari;Bahram Gharabaghi; "Remote Sensing Satellite Data Preparation for Simulating and Forecasting River Discharge," vol.56(6), pp.3432-3441, June 2018. Simulating and forecasting river discharge using satellite information is one of the most economical ways of measuring discharge, especially in remote areas where in situ gauges are too expensive to install, maintain, and operate. However, satellite signals are affected by climatic factors, such as clouds, mist, dust storms, and smoke from large forest fires. Hence, a reliable method of preparing and treating space-based signals is necessary. In this paper, a method is introduced to remove inaccurate and out-of-range signals having a low correlation with the in situ discharge measurements, using a combination of data classification and outlier detection procedures. To forecast future river discharge using space-based signals, the signal data set should not contain any gaps. Therefore, we introduced a procedure for the missing signals to be estimated by a model calibrated using the measured discharge. This procedure is illustrated using a case study for the White River, near Boston, MA, USA, that involved using a ddata set of three years’ daily signals of the passive microwave information from the Advanced Microwave Scanning Radiometer for Earth Observing System satellite, which are obtained from the difference between thermal emission of wet and dry land surfaces, and the in situ discharge measurements of the river. The best model was selected from 6200 available models using the Pareto front. The optimum model detected 339 samples as outliers and eliminated them from the data set. Subsequently, the signals were calibrated with the in situ information, and the results indicated the superior accuracy in simulating White River discharge using satellite information.

Maki Kikuchi;Hiroshi Murakami;Kentaroh Suzuki;Takashi M. Nagao;Akiko Higurashi; "Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite," vol.56(6), pp.3442-3455, June 2018. We developed a scheme to improve hourly estimates of aerosol optical thickness (AOT) from the Advanced Himawari Imager (AHI) onboard Himawari-8, the Japan Meteorological Agency’s latest geostationary satellite. Taking advantage of the sampling characteristics of the AHI (10-min temporal and subkilometer spatial resolution), we quantify temporal and spatial variability of AOT from the observations (AOToriginal) and utilize this information to develop an hourly algorithm that produces two sets of derived AOTs: AOTpure, derived by the application of strict cloud screening to AOToriginal, and AOTmerged, derived from spatial and temporal interpolations of AOTpure. The AOTs thus obtained from the hourly algorithm were validated against measurements from the aerosol robotic network. The root-mean-square errors (RMSEs) of the AOTpure and AOTmerged products were 0.14 and 0.11, respectively, providing improvement over an RMSE of 0.20 for AOToriginal.

Francescopaolo Sica;Davide Cozzolino;Xiao Xiang Zhu;Luisa Verdoliva;Giovanni Poggi; "InSAR-BM3D: A Nonlocal Filter for SAR Interferometric Phase Restoration," vol.56(6), pp.3456-3467, June 2018. The block-matching 3-D (BM3D) algorithm, based on the nonlocal approach, is one of the most effective methods to date for additive white Gaussian noise image denoising. Likewise, its extension to synthetic aperture radar (SAR) amplitude images, SAR-BM3D, is a state-of-the-art SAR despeckling algorithm. In this paper, we further extend BM3D to address the restoration of SAR interferometric phase images. While keeping the general structure of BM3D, its processing steps are modified to take into account the peculiarities of the SAR interferometry signal. Experiments on simulated and real-world Tandem-X SAR interferometric pairs prove the effectiveness of the proposed method.

Qian Shi;Xiaoping Liu;Xin Huang; "An Active Relearning Framework for Remote Sensing Image Classification," vol.56(6), pp.3468-3486, June 2018. Classification is an important technique for remote sensing data interpretation. In order to enhance the performance of a supervised classifier and ensure the lowest possible cost of the training samples used in the process, active learning (AL) can be used to optimize the training sample set. At the same time, integrating spatial information can help to enhance the separability between similar classes, which can in turn reduce the need for training samples in AL. To effectively integrate spatial information into the AL framework, this paper proposes a new active relearning (ARL) model for remote sensing image classification. In particular, our model is used to relearn the spatial features on the classification map, which contributes significantly to enhancing the performance of the classifier. We integrate the relearning model into the AL framework, with the aim to accelerate the convergence of AL and further reduce the labeling cost. Under the newly developed ARL framework, we propose two spatial–spectral uncertainty criteria to optimize the procedure for selecting new training samples. Furthermore, an adaptive multiwindow ARL model is also introduced in this paper. Our experiments with two hyperspectral images and two very high resolution images indicate that the ARL model exhibits faster convergence speed with fewer samples than traditional AL methods. Our results also suggest that the proposed spatial–spectral uncertainty criteria and the multiwindow version can further improve the performance when implementing ARL.

Ahmed Kiyoshi Sugihara El Maghraby;Angelo Grubišić;Camilla Colombo;Adrian Tatnall; "A Novel Interferometric Microwave Radiometer Concept Using Satellite Formation Flight for Geostationary Atmospheric Sounding," vol.56(6), pp.3487-3498, June 2018. For most Earth observation applications, passive microwave radiometry from the geostationary orbit requires prohibitively large apertures for conventional single-satellite platforms. This paper proposes a novel interferometric technique capable of synthesizing these apertures using satellite formation flight. The significance of such concept is in its capacity to synthesize microwave apertures of conceptually unconstrained size in space for the first time. The technique is implemented in two formation flight configurations: a formation of a single full-sized satellite with microsatellites and a formation of several full-sized satellites. Practical advantages and challenges of these configurations are explored by applying them to geostationary atmospheric sounding at 53 GHz, the lowest sounding frequency considered for future sounder concepts Geostationary Atmospheric Sounder, GeoSTAR, and Geostationary Interferometric Microwave Sounder. The two configurations produce apertures of 14.4 and 28.8 m, respectively, and a spatial resolution of 16.7 and 8.3 km, respectively, from the geostationary orbit. The performance of these interferometers is simulated, and the challenges identified are threefold. First, intersatellite ranging in micrometer-level precision is required. Second, the extremely sparse design suggests that further innovation is necessary to improve radiometric resolution. Third, the presence of long baselines suggests extreme decorrelation effects are expected. While the first requirement has already been demonstrated on ground, the other two remain for future research. This technique can be implemented at arbitrary microwave frequencies and arbitrary circular orbits, meaning it can also be applied to other geostationary applications, or to achieve unprecedented spatial resolution from lower orbits, or to extend the accessible frequencies into lower frequency radio waves.

Michael Newey;Gerald R. Benitz;David J. Barrett;Sandeep Mishra; "Detection and Imaging of Moving Targets With LiMIT SAR Data," vol.56(6), pp.3499-3510, June 2018. Detecting moving targets in synthetic aperture radar (SAR) imagery has recently gained a lot of interest as a way to augment optical moving target detection and classification in adverse (e.g., cloudy) weather conditions. In this paper, we primarily focus on the problem of detecting and imaging moving targets in single-channel (or summed multichannel) SAR data. Single-channel-based methods provide the ability to do detection well below the normal minimum detectable velocity (MDV) of multichannel-based GMTI. This can be particularly important for small radar antennas, which tend to have high conventional GMTI MDV. We also show results for multiple channel geolocation after single-channel detection and imaging. The algorithms consist of the following steps. We first suppress the stationary scene by comparing noncoherent time-subimages. We then detect the movers by applying a set of possible motion corrections to the image and use a novel matched filter to detect the movers in this space. We can then image the moving targets using standard SAR focusing techniques and geolocate the movers using multichannel (if available) along-track interferometry. We demonstrate and evaluate our algorithms using data collected from the Lincoln Multimission ISR Testbed airborne radar system.

Kenji Nakamura;Yuki Kaneko;Katsuhiro Nakagawa;Hiroshi Hanado;Masanori Nishikawa; "Measurement Method for Specific Attenuation in the Melting Layer Using a Dual Ka-Band Radar System," vol.56(6), pp.3511-3519, June 2018. An estimation method for specific attenuation and equivalent radar reflectivity in the melting layer using a dual Ka-band radar system was studied. The system consists of two identically designed Ka-radars. When a precipitation system comes between the two radars, the radars observe the system from opposite directions. The radar applies the frequency-modulated continuous-wave type and is designed for the measurement of the scattering characteristics of precipitation particles. The precipitation echoes suffer from rain attenuation. The reduction due to rain attenuation symmetrically appears in both radar echoes. By differentiating averaged measured radar reflectivity with range, the specific attenuation can be estimated. After obtaining the specific attenuation, equivalent radar reflectivity is estimated. In the melting layer, specific attenuation and the equivalent radar reflectivity vary largely along the radio path, and the estimated specific attenuation is very sensitive to the setup configuration of the experiment. The accuracy of the estimated specific attenuation was found to depend on the curvature, that is, the doubly differentiated value of the equivalent radar reflectivity with respect to range and the distance for the differentiation. The measurement system and actual procedure for the data analysis are also described.

Paul D. Ledger;William R. B. Lionheart; "An Explicit Formula for the Magnetic Polarizability Tensor for Object Characterization," vol.56(6), pp.3520-3533, June 2018. The magnetic polarizability tensor (MPT) has attracted considerable interest due to the possibility it offers for characterizing conducting objects and assisting with the identification and location of hidden targets in metal detection. An explicit formula for its calculation for arbitrary-shaped objects is missing in the electrical engineering literature. Furthermore, the circumstances for the validity of the magnetic dipole approximation of the perturbed field, induced by the presence of the object, are not fully understood. On the other hand, in the applied mathematics community, an asymptotic expansion of the perturbed magnetic field has been derived for small objects and a rigorous formula for the calculation of the MPT has been obtained. The purpose of this paper is to relate the results of the two communities, to provide a rigorous justification for the MPT, and to explain the situations in which the approximation is valid.

Leyuan Fang;Nanjun He;Shutao Li;Antonio J. Plaza;Javier Plaza; "A New Spatial–Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation," vol.56(6), pp.3534-3546, June 2018. In this paper, a novel local covariance matrix (CM) representation method is proposed to fully characterize the correlation among different spectral bands and the spatial–contextual information in the scene when conducting feature extraction (FE) from hyperspectral images (HSIs). Specifically, our method first projects the HSI into a subspace, using the maximum noise fraction method. Then, for each test pixel in the subspace, its most similar neighboring pixels (within a local spatial window) are clustered using the cosine distance measurement. The test pixel and its neighbors are used to calculate a local CM for FE purposes. Each nondiagonal entry in the matrix characterizes the correlation between different spectral bands. Finally, these matrices are used as spatial–spectral features and fed to a support vector machine for classification purposes. The proposed method offers a new strategy to characterize the spatial–spectral information in the HSI prior to classification. Experimental results have been conducted using three publicly available hyperspectral data sets for classification, indicating that the proposed method can outperform several state-of-the-art techniques, especially when the training samples available are limited.

Qiang Zhao;Qizhen Du;Xufei Gong;Yangkang Chen; "Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter," vol.56(6), pp.3547-3560, June 2018. Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. In this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion is replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.

Patrick Henkel;Franziska Koch;Florian Appel;Heike Bach;Monika Prasch;Lino Schmid;Jürg Schweizer;Wolfram Mauser; "Snow Water Equivalent of Dry Snow Derived From GNSS Carrier Phases," vol.56(6), pp.3561-3572, June 2018. Snow water equivalent (SWE) is a key variable for various hydrological applications. It is defined as the depth of water that would result upon complete melting of a mass of snow. However, until now, continuous measurements of the SWE are either scarce, expensive, labor-intense, or lack temporal or spatial resolution especially in mountainous and remote regions. We derive the SWE for dry-snow conditions using carrier phase measurements from the Global Navigation Satellite System (GNSS) receivers. Two static GNSS receivers are used, whereby one antenna is placed below the snow and the other antenna is placed above the snow. The carrier phase measurements of both receivers are combined in double differences (DDs) to eliminate clock offsets and phase biases and to mitigate atmospheric errors. Each DD carrier phase measurement depends on the relative position between both antennas, an integer ambiguity due to the periodic nature of the carrier phase signal, and the SWE projected into the direction of incidence. The relative positions of the antennas are determined under snow-free conditions with millimeter accuracy using real-time kinematic positioning. Subsequently, the SWE and carrier phase integer ambiguities are jointly estimated with an integer least-squares estimator. We tested our method at an Alpine test site in Switzerland during the dry-snow season 2015–2016. The SWE derived solely by the GNSS shows very high correlation with conventionally measured snow pillow (root mean square error: 11 mm) and manual snow pit data. This method can be applied to dense low-cost GNSS receiver networks to improve the spatial and temporal information on snow.

Wenkang Liu;Guang-Cai Sun;Xiang-Gen Xia;Jianlai Chen;Liang Guo;Mengdao Xing; "A Modified CSA Based on Joint Time-Doppler Resampling for MEO SAR Stripmap Mode," vol.56(6), pp.3573-3586, June 2018. Image formation of large scenes is still challenging in medium-earth-orbit (MEO) synthetic aperture radar (SAR) due to the existence of severe 2-D space variance. In this paper, the properties of space variance are analyzed in detail, and then a variable-coefficient fourth-order range model is adopted to model the space-variant range history of every target in a large scene accurately. A method integrating a modified chirp scaling algorithm with joint time-Doppler resampling is proposed to address the range-variant range cell migration, as well as the azimuth-variant frequency-modulation rate and higher order Doppler parameters. The computational burden and alternative implementation approaches are also discussed. Finally, processing of simulated data for MEO SAR with 2-m resolution is presented to validate the proposed algorithm.

Wuxia Zhang;Xiaoqiang Lu;Xuelong Li; "A Coarse-to-Fine Semi-Supervised Change Detection for Multispectral Images," vol.56(6), pp.3587-3599, June 2018. Change detection is an important technique providing insights to urban planning, resources monitoring, and environmental studies. For multispectral images, most semi-supervised change detection methods focus on improving the contribution of training samples hard to be classified to the trained classifier. However, hard training samples will weaken the discrimination of the training model for multispectral change detection. Besides, these methods only use the spectral information, while the limited spectral information cannot represent objects very well. In this paper, a method named as coarse-to-fine semi-supervised change detection is proposed to solve the aforementioned problems. First, a novel multiscale feature is exploited by concatenating the spectral vector of the pixel to be detected and its adjacent pixels by different scales. Second, the enhanced metric learning is proposed to acquire more discriminant metric by strengthening the contribution of training samples easy to be classified and weakening the contribution of training samples hard to be classified to the trained model. Finally, a coarse-to-fine strategy is adopted to detect testing samples from the viewpoint of distance metric and label information of neighborhood in spatial space. The coarse detection result obtained from the enhanced metric learning is used to guide the final detection. The effectiveness of our proposed method is verified on two real-life operating scenarios, Taizhou and Kunshan data sets. Extensive experimental results demonstrate that our proposed algorithm has better performance than those of other state-of-the-art algorithms.

Zefa Yang;Zhiwei Li;Jianjun Zhu;Axel Preusse;Jun Hu;Guangcai Feng;Markus Papst; "Time-Series 3-D Mining-Induced Large Displacement Modeling and Robust Estimation From a Single-Geometry SAR Amplitude Data Set," vol.56(6), pp.3600-3610, June 2018. This paper presents a novel method for modeling and robustly estimating the time-series 3-D mining-induced large displacements from a single imaging geometry (SIG) synthetic aperture radar (SAR) amplitude data set using the offset-tracking (OT) technique (hereafter referred to as the OT-SIG). It first generates multitemporal observations of 3-D mining-induced displacements from the single-geometry SAR amplitude data set with the assistance of a prior model. Then, a functional relationship between mining-induced time-series 3-D displacements and the multitemporal 3-D deformation observations generated is constructed. Finally, the time-series 3-D displacements are robustly estimated based on the constructed function model using the M-estimator. The proposed OT-SIG provides a robust and cost-effective tool for retrieving time-series 3-D mining-induced large displacements, relaxing the basic requirement of the traditional method that at least two different viewing geometries’ SAR data are needed. Finally, we tested the proposed OT-SIG with descending TerraSAR-X SAR amplitude data set over the Daliuta coal mining area in China. The results show that the root-mean-square errors (RMSEs) of OT-SIG-estimated time-series displacements are about 0.22 and 0.11 m in the vertical and horizontal directions, respectively. These RMSEs are around 5.7% and 10.9% of the maximum in situ deformation measurements in the corresponding directions, which can meet the accuracy requirements of practical applications.

Elías Méndez Domínguez;Erich Meier;David Small;Michael E. Schaepman;Lorenzo Bruzzone;Daniel Henke; "A Multisquint Framework for Change Detection in High-Resolution Multitemporal SAR Images," vol.56(6), pp.3611-3623, June 2018. Change detection from multitemporal synthetic aperture radar (SAR) images enables mapping applications for earth environmental observation, human activity monitoring, and urban studies. We expand the use of SAR data beyond single-look processing to include the spatial response of targets. This information is derived from a multisquint framework similar to beamforming. To preserve changes detected at nominal resolution, a three-stage change detector exploiting single-look and multisquint processing mode is proposed to mitigate false alarms caused by image artifacts typically found in high-resolution SAR imagery and urban scenarios. After applying the proposed method to multitemporal images, the false alarm rate was reduced by a factor 3, while preserving 95% of the detection rate offered by traditional schemes.

Hankui K. Zhang;David P. Roy;Valeriy Kovalskyy; "Correction to “Optimal Solar Geometry Definition for Global Long-Term Landsat Time-Series Bidirectional Reflectance Normalization”," vol.56(6), pp.3624-3624, June 2018. The following typographical errors are present in the right-hand side of the following equation of the paper [1]:

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Alexey A. Kolchev;Ivan A. Egoshin; "Use of Hazard Function for Signal Detection on Ionograms," vol.15(6), pp.803-807, June 2018. This letter considers a new method for sample detection of ionospheric propagation modes at the receiver output of a chirp ionosonde. The method is based on the use of the hazard function. Within the framework of the two-component mixture model, it is shown that the hazard function can be used to detect samples of the second component in the mixture when the corresponding fraction of samples in the total number is small. The implementation of the method is carried out using training sets. An average normalized hazard function is constructed for the training set not containing the signal. Normalization is carried out in such a way that the hazard function can be considered as a probability density function of a certain random variable. We used Pearson’s statistic to test the agreement between the average normalized hazard function of samples that do not contain the signal and an arbitrary normalized hazard function. The critical value of the statistic was determined using the Neyman–Pearson criterion. The effectiveness of the proposed method has been tested for the determination of the lowest and maximum observed frequencies of radio paths from oblique ionospheric sounding ionograms obtained in the Russian network of circumpolar radio paths. The method suggested in this letter can be used not only when processing ionograms but also for the detection of samples of signals of arbitrary nature against a background noise, provided that statistical signal-to-noise ratios are similar to those considered here.

Nobuyuki Utsumi;Hyungjun Kim; "Warm Season Satellite Precipitation Biases for Different Cloud Types Over Western North Pacific," vol.15(6), pp.808-812, June 2018. Two along-track (level 2) satellite precipitation retrievals by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Dual Frequency Precipitation Radar Ku-band (DPR-Ku) and two multisatellite precipitation products, global satellite mapping of precipitation (GSMaP) and Integrated Multisatellite Retrievals for GPM (IMERG), are intercompared for different cloud types during warm season over the western North Pacific region. It is found that the biases of the precipitation measurements are systematically associated with cloud types. The best agreements of passive microwave (PMW) products and infrared-based (IR) products with satellite radar-based estimates are found for a relatively weak precipitation range for mid-low clouds (except over land) and high clouds, while similar agreement is found for heavier precipitation range for deep convection regardless of surface type. Precipitation from mid-low clouds over land is considerably underestimated by PMW and IR products over almost the entire intensity range. The IR-based precipitation estimates for deep convective clouds considerably overestimate the intensity for both weak precipitation and cases where precipitation was not detected by the DPR-Ku algorithm. The findings reveal the characteristics of the biases of the products depend on the associated cloud types, which suggests consideration of the cloud type information to improve satellite-based precipitation estimates.

Furkan Isikdogan;Alan Bovik;Paola Passalacqua; "Learning a River Network Extractor Using an Adaptive Loss Function," vol.15(6), pp.813-817, June 2018. We have created a deep-learning-based river network extraction model, called DeepRiver, that learns the characteristics of rivers from synthetic data and generalizes them to natural data. To train this model, we created a very large database of exemplary synthetic local channel segments, including channel intersections. Our model uses a special loss function that automatically shifts the focus to the hardest-to-learn parts of an input image. This adaptive loss function makes it possible to learn to detect river centerlines, including the centerlines at junctions and bifurcations. DeepRiver learns to separate between rivers and oceans, and therefore, it is able to reliably extract rivers in coastal regions. The model produces maps of river centerlines, which have the potential to be quite useful for analyzing the properties of river networks.

Jin Zheng;Qizhi Xu;Jie Chen;Cunguang Zhang; "The On-Orbit Noncloud-Covered Water Region Extraction for Ship Detection Based on Relative Spectral Reflectance," vol.15(6), pp.818-822, June 2018. For most of the existing noncloud-covered water region (NCC water region) extraction methods, they are designed for on-ground ship detection. However, these methods could result in low accuracy and expensive computational costs. In this letter, an accurate and fast NCC water region extraction method is proposed for the typical on-orbit ship detection system with limited computing resources available. According to the relative spectral reflectance difference of water, land, and thick cloud, a triple-peak model is first constructed. Next, the parameters of the triple-peak model can be adaptively obtained based on the correlation of the previous adjacent image blocks acquired from the data stream of the panchromatic camera. Subsequently, the accurate NCC water region can be quickly extracted. Moreover, the proposed method is validated using a large amount of raw data captured by panchromatic satellite cameras. The experimental results on a Xilinx-5VFX130t field-programmable gate array imply that the proposed method performs well in the NCC water region extraction with the precision of 99.3% and recall of 99.5%, and it is suitable for on-orbit processing.

Dapeng Mu; "Instantaneous Rate of Ice Mass Changes in Antarctica Observed by Satellite Gravimetry," vol.15(6), pp.823-827, June 2018. A method for computing instantaneous rate of the Antarctica ice sheet (AIS) mass changes is proposed in this letter. The method combines the ensemble empirical mode decomposition (EEMD) and cubic spline smoothing (CSS). First, EEMD is used to extract the nonlinear trend from the time series of the AIS mass changes observed by satellite gravimetry, and then CSS is used to fit the nonlinear trend. The first-order derivative from the fit curve is the instantaneous rate. Compared with the linear rate, the instantaneous rate improves the temporal resolution of mass change rate. The instantaneous rate reveals the complex behavior of the AIS mass changes; at regional scale, mass changes show negative rate for one period and positive rate for another. This important information is masked by the linear rate which only suggests an overall increasing or decreasing trend. The instantaneous rate promotes our understanding of the mass changes in the AIS and shows implication for detecting the anomalous changes in climate.

Bing Wang;Kuo Zhang; "Direct Inversion Algorithm for Shear Velocity Profiling in Dipole Acoustic Borehole Measurements," vol.15(6), pp.828-832, June 2018. The evaluation of radial heterogeneities within formations plays an important role in oil and gas exploration and exploitation. Dipole acoustic well logging data are widely used to invert radial shear velocity profiles, for which efficient dispersion analysis algorithms and radial profile inversion algorithms constitute fundamental issues. A direct parametric inversion method is, therefore, proposed in this letter, and a phase-based dispersion method is employed to calculate the dispersion curves of measured dipole acoustic data. The accuracy and computational efficiency of the inversion can be simultaneously guaranteed by using the proposed dispersion analysis method. An exponential radial profile function with three parameters, namely, the relative change in the shear modulus, the decaying parameter of the exponential function, and the radial extent of the perturbation zone, is used to describe alterations in formation shear wave velocities. Formations with different alteration characteristics can be expressed effectively using this proposed exponential function. The cost function is then calculated in terms of the three exponential function parameters and the real data dispersion curve. After the three parameters are inverted by finding the minimum value of the cost function, the radial shear wave velocity profile can be expressed using the exponential function. Finally, processing results using synthetic data confirm the efficiency of the proposed algorithm.

Jesper Martinsson;Adam Jonsson; "A New Model for the Distribution of Observable Earthquake Magnitudes and Applications to <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>-Value Estimation," vol.15(6), pp.833-837, June 2018. The <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>-value in the Gutenberg–Richter (GR) law contains information that is essential for evaluating earthquake hazard and predicting the occurrence of large earthquakes. Estimates of <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> are often based on seismic events whose magnitude exceed a certain threshold, the so-called magnitude of completeness. Such estimates are sensitive to the choice of threshold and often ignore a substantial portion of available data. We present a general model for the distribution of observable earthquake magnitudes and an estimation procedure that takes all measurements into account. The model is obtained by generalizing previous probabilistic descriptions of sensor network limitations and using a generalization of the GR law. We show that our model is flexible enough to handle spatio-temporal variations in the seismic environment and captures valuable information about sensor network coverage. We also show that the model leads to significantly improved <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>-value estimates compared with established methods relying on the magnitude of completeness.

Martina T. Bevacqua;Tommaso Isernia; "Boundary Indicator for Aspect Limited Sensing of Hidden Dielectric Objects," vol.15(6), pp.838-842, June 2018. This letter addresses the problem of reconstructing the geometrical features, i.e., location, shape, and size of targets embedded into a nonaccessible region. In particular, an approach recently introduced for the case of free space and full aspect measurements is extended, discussed, and validated for the more realistic and challenging case of data collected under “aspect limited” measurement configurations, which include subsurface sensing, cross-borehole imaging, and many other cases. Results obtained by considering 2-D scenario and noise affected simulated data confirm the potentialities of the proposed method in dealing with realistic applications.

Shengwu Qin;Zhongjun Ma;Jun Lin;Yiguo Xue;Chuandong Jiang;Xinlei Shang;Zhiqiang Li; "New Method for Detecting Risk of Tunnel Water-Induced Disasters Using Magnetic Resonance Sounding," vol.15(6), pp.843-847, June 2018. A new method for detecting risk of tunnel water-induced disasters using magnetic resonance sounding (MRS) is proposed in this letter. The method utilizes magnetic resonance signals that are generated directly from hydrogen protons to achieve the purpose of detecting risk of tunnel water-induced disasters directly and quantitatively. This letter evaluates the potential of this method based on a systematic study involving forward modeling, numerical experiments, and a large-scale physical model test. The relationship between the magnetic resonance signal response in the tunnel and the position and water content of water-bearing structures is obtained by the forward modeling. In the numerical examples, the inversion results are in agreement with the synthetic model. In the physical model test, the inversion results can accurately locate the water-bearing structure, and the water content linearly decreases with the water level of the water-bearing structure, which verifies the feasibility and effectiveness of predictions made based on the MRS data. These results demonstrate that tunnel MRS can be used to anticipate water-induced disasters.

Wenfei Cao;Yi Chang;Guodong Han;Junbing Li; "Destriping Remote Sensing Image via Low-Rank Approximation and Nonlocal Total Variation," vol.15(6), pp.848-852, June 2018. Stripe noise removal is a fundamental problem in remote sensing image processing. Many efforts have been made to resolve this problem. Recently, a state-of-the-art method was proposed from image-decomposition perspective. This method argued that the stripe and clear image can be simultaneously estimated by modeling the directional structure of stripes and the local smoothness of remote sensing images. However, the potential of this method cannot be fully delivered when confronting with dense stripes with high intensity. In this letter, we further consider the nonlocal self-similarity of image patches in the spatiospectral volume in terms of nonlocal total variation and propose a method of better robustness to dense stripes. Experimental results on both synthetic and real multispectral data show that the proposed method outperforms other competing methods in the remote sensing image destriping task.

Felipe Queiroz de Almeida;Marwan Younis;Gerhard Krieger;Alberto Moreira; "An Analytical Error Model for Spaceborne SAR Multichannel Azimuth Reconstruction," vol.15(6), pp.853-857, June 2018. In the context of spaceborne synthetic aperture radar (SAR) for remote sensing, multichannel system architectures coupled with digital beamforming techniques are deemed a necessary technological advancement to fulfill the requirements for near-future spaceborne radar missions. Calibration of such systems is an important topic, since channel imbalances may lead to considerable degradation of their performance. This letter analyzes the impact of residual errors in a SAR system with multiple channels in azimuth and derives an analytical model for the resulting performance degradation, which may be used in system design as an aid to establish requirements in an error budget analysis.

Yanfeng Dang;Yi Liang;Bowen Bie;Jinshan Ding;Yuhong Zhang; "A Range Perturbation Approach for Correcting Spatially Variant Range Envelope in Diving Highly Squinted SAR With Nonlinear Trajectory," vol.15(6), pp.858-862, June 2018. The image processing for diving highly squinted synthetic aperture radar (SAR) mounted on a maneuvering platform with nonlinear trajectory is challenging due to the spatial variance in the range envelope along azimuth, which is caused by the inherent range dependence of squint angle, platform acceleration, and the range cell migration (RCM). In order to deal with the spatial variance that leads to defocusing in SAR imaging, a range perturbation approach is proposed in the frequency domain to mitigate the azimuth-dependent RCM curvatures, so that the RCM can be uniformly compensated for in the 2-D frequency domain. Moreover, this approach is totally interpolation free, and thus the computation load is reduced dramatically. The effectiveness of the proposed approach is confirmed and demonstrated via simulations.

Ke Tan;Wenchao Li;Jifang Pei;Yulin Huang;Jianyu Yang; "An I/Q-Channel Modeling Maximum Likelihood Super-Resolution Imaging Method for Forward-Looking Scanning Radar," vol.15(6), pp.863-867, June 2018. Deconvolution techniques provide efficient implementations for super-resolution imaging for forward-looking scanning radar. However, deconvolution is normally an ill-posed problem, and the solution is extremely sensitive to noise. From a statistical perspective, maximum likelihood (ML) methods are able to condition the ill-posed problem into a well-posed one. Nevertheless, traditional ML methods only consider the amplitude of the echo and by ignoring the phase that do not adequately model the radar imaging system. In this letter, an I/Q-channel modeling ML method is proposed for forward-looking scanning radar. First, the probability model of the echo is deduced by jointly considering noise in the I and Q channels. Then, a probability density function of the received data is deduced and used to formulate the likelihood function. Finally, the targets can be precisely estimated by maximizing this likelihood function. The results of simulations and experiments are provided to illustrate the effectiveness of the proposed method.

Shisheng Guo;Xiaobo Yang;Guolong Cui;Yilin Song;Lingjiang Kong; "Multipath Ghost Suppression for Through-the-Wall Imaging Radar via Array Rotating," vol.15(6), pp.868-872, June 2018. In this letter, we consider the problem of multipath ghost suppression for through-the-wall imaging radar. Exploiting the fact that these locations of the multipath ghosts depend on while the target location is independent of the array configuration, we present a novel framework via array rotating to eliminate the multipath ghosts. Specifically, we first rotate the array with multiple different array rotation angles. Then, multiple images are derived using back-projection imaging algorithm. Finally, the incoherent arithmetic fusion method is applied to yield a ghost-free image. The proposed approach has two advantages for multipath ghost suppression. First is the simplicity of the operation, and the second is that it will not be affected by the incorrect wall parameters. Ghost suppression performance of the proposed approach is evaluated via numerical simulations.

Stanisława Porzycka-Strzelczyk;Paweł Rotter;Jacek Strzelczyk; "Automatic Detection of Subsidence Troughs in SAR Interferograms Based on Circular Gabor Filters," vol.15(6), pp.873-876, June 2018. This letter presents a method of automatic detection of subsidence troughs in the synthetic aperture radar (SAR) interferograms. Construction of such an algorithm is motivated by the very large amount of freely available SAR imagery from Sentinel mission. The proposed method is based on the convolution of the SAR interferogram with a bank of circular wavelets. Spatial locations with high values of the convolved images correspond to centers of troughs. The method has been tested on differential interferograms generated based on Sentinel-1 SAR images. The detection rate varies from 30% to 53% with a relatively low number of false alarms.

Baolong Wu;Ling Tong;Yan Chen; "Revised Improved DINSAR Algorithm for Monitoring the Inclination Displacement of Top Position of Electric Power Transmission Tower," vol.15(6), pp.877-881, June 2018. In the electric power transmission corridor areas, the ground surface deformation will influence the inclination of the electric power transmission towers even after the collapse of the towers. Due to the layover, traditional differential synthetic aperture radar interferometry (DINSAR) cannot be used to obtain the inclination displacement of the general line shape and vertical coherent objects such as electric power transmission towers fixed vertically in the ground. Based on the improved DINSAR (IM-DINSAR) model, this letter proposes a revised implementing algorithm for IM-DINSAR model. It can remove the top-bottom vertical-height interferometry phase of the tower (caused by the height of the tower itself, similar to the flat earth interferometry phase in traditional DINSAR) by only using the information of every tower itself. This is different from the implementing algorithm proposed. Then, we use the residual differential interferometry phase after phase unwrapping to obtain the inclination displacement of top position of the tower. Moreover, this letter analyzes atmospheric phase, noise, and multitemporal series cases in IM-DINSAR model. Simulation results demonstrate the effectiveness of the model proposed in this letter.

Kangyu Zhang;Jingfeng Huang;Xiazhen Xu;Qiaoying Guo;Yaoliang Chen;Lamin R. Mansaray;Zhengquan Li;Xiuzhen Wang; "Spatial Scale Effect on Wind Speed Retrieval Accuracy Using Sentinel-1 Copolarization SAR," vol.15(6), pp.882-886, June 2018. High-spatial-resolution wind fields derived from synthetic aperture radar (SAR) instruments are crucial to a wide range of applications. However, the spatial scale effect on wind speed retrieval accuracy has seldom been reported. For the purpose of understanding this issue, this letter makes a quality assessment of wind speed retrieval accuracy based on four commonly used C-band geophysical model functions (CMOD4, CMOD-IFR2, CMOD5, and CMOD5.N) at spatial resolutions ranging from 50 m to 50 km using Sentinel-1 interferometric wide (IW) swath mode images. Our results show that the CMOD5 function is the most effective among these functions, owing to a low root-mean-square error (RMSE) of 1.17 m/s and a bias of −0.28 m/s for wind speed retrieval at a spatial resolution of 500 m. It is further observed that the variance of wind speeds retrieved from copolarized SAR images decreases exponentially with the decrease of spatial resolutions. For Sentinel-1 IW mode images, the variance of wind speeds retrieved with CMOD5 decreases rapidly from 50 to 500 m, with a drop in RMSE of 40%, and thereafter levels off. Thus, a spatial resolution of 500 m, with the CMOD5 function, is recommended optimal in this letter, for wind speed retrieval using Sentinnel-1 IW mode data.

Tayebe Managhebi;Yasser Maghsoudi;Mohammad Javad Valadan Zoej; "An Improved Three-Stage Inversion Algorithm in Forest Height Estimation Using Single-Baseline Polarimetric SAR Interferometry Data," vol.15(6), pp.887-891, June 2018. This letter provides an advanced method to improve the result for three-stage inversion algorithm, using polarimetric synthetic aperture radar interferometry (PolInSAR) technique based on the random volume over ground model. In the conventional three-stage method, the ground phase, extinction coefficient, and volume layer height are estimated in a geometrical way without the need for a prior information or separate reference digital elevation model. The extinction and volume height estimation is done in the third stage by searching in the 2-D area. In the proposed algorithm, defining a new geometrical index, according to signal penetration in the forest, imposes a limited range for the extinction coefficient. The new index, as an axillary data, helps make searching limited to a reasonable 2-D area. The proposed algorithm was applied on L-band E-SAR single-baseline single-frequency PolInSAR data. As a result of applying this restriction in the extinction range, a 2.5-m improvement was observed in the RMSE of the proposed algorithm compared with that of the conventional three-stage method.

Nida Sakar;Marc Rodriguez-Cassola;Pau Prats-Iraola;Andreas Reigber;Alberto Moreira; "Analysis of Geometrical Approximations in Signal Reconstruction Methods for Multistatic SAR Constellations With Large Along-Track Baseline," vol.15(6), pp.892-896, June 2018. Large along-track baselines introduce residual polychromatic quadratic phase components which decrease the performance of state-of-the-art multichannel/multiplatform SAR reconstruction algorithms. This letter investigates the impact of the geometrical approximations in signal reconstruction methods for spaceborne multistatic SAR constellations with large along-track baselines operated with a pulse repetition frequency (PRF) under the Nyquist rate required for a single platform. We characterize and quantify the impact of these approximations, especially severe in the case of kilometric baselines and resolutions around <inline-formula> <tex-math notation="LaTeX">$15lambda$ </tex-math></inline-formula>. Finally, we put forward a generalized range-Doppler strategy to accommodate the geometry of distributed along-track constellations in an accurate manner.

Yili Zhao;Alexis A. Mouche;Bertrand Chapron;Nicolas Reul; "Direct Comparison Between Active C-Band Radar and Passive L-Band Radiometer Measurements: Extreme Event Cases," vol.15(6), pp.897-901, June 2018. Co-located over extreme events, C-band co-polarized and cross-polarized normalized radar cross sections (NRCS) and L-band ocean surface roughness brightness temperature (<inline-formula> <tex-math notation="LaTeX">$T_{B,text {rough}}$ </tex-math></inline-formula>) are directly compared to analyze the similarities and differences between these two parameters at medium resolution (about 25 km). NRCS in VH-polarization and VV-polarization (<inline-formula> <tex-math notation="LaTeX">$sigma _{0,mathrm {VH}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$sigma _{0,mathrm {VV}}$ </tex-math></inline-formula>) were acquired by Sentinel-1 C-band synthetic aperture radar. <inline-formula> <tex-math notation="LaTeX">$T_{B,mathrm {rough}}$ </tex-math></inline-formula> is estimated from brightness temperatures (<inline-formula> <tex-math notation="LaTeX">$T_{B}$ </tex-math></inline-formula>) measured by the L-band radiometer on-board the Soil Moisture Active Passive mission. When the rain rate is less than 20 mm/h, a striking linear relationship is found between active C-Band cross-polarized NRCS and passive L-Band <inline-formula> <tex-math notation="LaTeX">$T_{Bmathrm {,rough}}$ </tex-math></inline-formula>: <inline-formula> <tex-math notation="LaTeX">$sigma _{0,mathrm {VH}}(theta _{mathrm {SAR}}) propto tan (theta _{mathrm {SAR}}) times T_{B,mathrm {rough}}(theta _{mathrm {SMAP}}= 40^{circ }$ </tex-math></inline-formula>), without any apparent saturation for <inline-formula> <tex-math notation="LaTeX">$T_{B,mathrm {rough}}$ </tex-math></inline-formula> ranging from 3.5 to 17 K. Compared to both high <inline-formula> <tex-math notation="LaTeX">$T_{B,mathrm {rough}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$sigma _{0,mathrm {VH}}$ </tex-math></inline-formula>, co-polarized <inline-formula> <tex-math notation="LaTeX">$sigma _{0,mathrm {VV}}$ </tex-math></inline-formula> measurements satu- ate. As interpreted, this can correspond to a regime change of the air–sea interactions during extreme events. In heavy rain conditions, C-band co-polarized NRCS decreases for extreme situations. In these cases, the covariation between C-band cross-polarized NRCS and L-band <inline-formula> <tex-math notation="LaTeX">$T_{B,mathrm {rough}}$ </tex-math></inline-formula> is less evident. An accurate and unambiguous assessment of the impact of rain will deserve further investigations.

Dawei Li;Donald R. Wilton;David R. Jackson;Hanming Wang;Ji Chen; "Accelerated Computation of Triaxial Induction Tool Response for Arbitrarily Deviated Wells in Planar-Stratified Transversely Isotropic Formations," vol.15(6), pp.902-906, June 2018. In this letter, a new formulation for calculating an induction logging tool response in a multilayered transversely isotropic (also called uniaxial anisotropic) dipping formation is proposed. Using transmission line theory, only five independent integrals are necessary to form all the components of the dyadic Green’s function in order to calculate the tool response, resulting in a more succinct and efficient formulation. The fast Hankel transform (FHT) is then employed to calculate these spectral integrals efficiently. In special cases, such as horizontal and vertical wells where the direct FHT fails, alternative approaches are proposed. A singularity extraction is implemented to handle the divergent integrals for a horizontal well, whereas the double-exponential integration rule is implemented for a vertical well. In order to speed up the computation, the spectral transmission line currents and voltages are precalculated and then stored in memory, and parallel computing using OpenMP is employed. Numerical examples demonstrate a speedup factor of more than two orders of magnitude compared with other reported results based solely on the FHT.

Yufang Li;Qingxia Li;Li Feng; "Analysis of Reflector Sizes in Rotating Mirrored Aperture Synthesis Radiometers," vol.15(6), pp.907-911, June 2018. Rotating mirrored aperture synthesis (RMAS) has been proposed as a new method for high spatial resolution earth observation from the geostationary orbit. However, in previous researches, the reflector sizes are assumed to be infinite so that all antennas in the antenna array can receive the reflection signals. This assumption is obviously unacceptable in practical applications. How to estimate the reflector sizes is still a problem. In this letter, the principle of RMAS is briefly reviewed. Then, the reflector sizes of an RMAS radiometer are analyzed based on solid geometry, and detailed calculation formulae are derived. A numerical example is given for calculating the reflector sizes of an RMAS radiometer applied in the geostationary orbit.

Hongmei Liu;Decheng Hong;Na Li;Wei Han;Qing Huo Liu; "Solving Electromagnetic Fields by General ReflectionTransmission Method for Coaxial-Coil Antenna in Cylindrically Multilayered Medium," vol.15(6), pp.912-916, June 2018. In this letter, we present a set of compact and no-overflow formulations to calculate the electromagnetic (EM) fields from coaxial coil antennas in a concentric cylindrically multilayered medium. It can be applied to fast forward computation and the inverse problem for EM well logging. The derivation is performed by using the tangential component of electric field. In contrast with previous formulations, the adopted novel reflection and transmission coefficients are scalars rather than matrices, which make it easy to get an accurate and efficient Jacobian matrix for inversion problem. The basic unit of the formulations is the ratio of the cylindrical functions so that the notorious overflow problem for numerical computation can be obviated. Numerical results in comparison with those from other approaches have demonstrated the validity and stability of our new formulations for forward modeling. To show the potential of the proposed formulations, an inverse result from a simple formation model is also presented.

Ghassem Khademi;Hassan Ghassemian; "Incorporating an Adaptive Image Prior Model Into Bayesian Fusion of Multispectral and Panchromatic Images," vol.15(6), pp.917-921, June 2018. Reconstruction of a sharpened multispectral (MS) image from its coarser measurements, namely the low spatial resolution MS and panchromatic (Pan) images, is a severely illposed inverse problem which requires the definition of an appropriate prior model. This letter incorporates an adaptive Markov random field (MRF)-based prior model into a Bayesian framework to recover the desired MS image. The proposed MRF-based prior model combines the high frequency details of the Pan image with the spectral relation between the bands of the MS image into a single energy function. Consequently, unlike most pansharpening methods, not directly injecting the spatial information of the Pan image into the fused product, the proposed method offers a fused product with minimum spectral distortion, along with perfectly enhanced spatial resolution. Visual and quantitative assessments of the fused products of the proposed method compared to those of some famous pansharpening methods prove the superiority of the proposed method.

Christian Geiß;Matthias Thoma;Hannes Taubenböck; "Cost-Sensitive Multitask Active Learning for Characterization of Urban Environments With Remote Sensing," vol.15(6), pp.922-926, June 2018. We propose a novel cost-sensitive multitask active learning (CSMTAL) approach. Cost-sensitive active learning (CSAL) methods were recently introduced to specifically minimize labeling efforts emerging from ground surveys. Here, we build upon a CSAL method but compile a set of unlabeled samples from a learning set which can be considered relevant with respect to multiple target variables. To this purpose, a multitask meta-protocol based on alternating selection is implemented. It comprises a so-called one-sided selection (i.e., single-task AL selection for a reference target variable with simultaneous labeling of the residual target variables) with a changing leading variable in an iterative selection process. Experimental results are obtained for the city of Cologne, Germany. The target variables to be predicted, using features from remote sensing and a support vector machine framework, are “building type” and “roof type.” Comparative model accuracy evaluations underline the capability of the CSMTAL method to provide beneficial solutions with respect to a random sampling strategy and noncost-sensitive multitask active sampling.

Nabil Zerrouki;Fouzi Harrou;Ying Sun; "Statistical Monitoring of Changes to Land Cover," vol.15(6), pp.927-931, June 2018. Accurate detection of changes in land cover leads to better understanding of the dynamics of landscapes. This letter reports the development of a reliable approach to detecting changes in land cover based on remote sensing and radiometric data. This approach integrates the multivariate exponentially weighted moving average (MEWMA) chart with support vector machines (SVMs) for accurate and reliable detection of changes to land cover. Here, we utilize the MEWMA scheme to identify features corresponding to changed regions. Unfortunately, MEWMA schemes cannot discriminate between real changes and false changes. If a change is detected by the MEWMA algorithm, then we execute the SVM algorithm that is based on features corresponding to detected pixels to identify the type of change. We assess the effectiveness of this approach by using the remote-sensing change detection database and the SZTAKI AirChange benchmark data set. Our results show the capacity of our approach to detect changes to land cover.

Alina Majeed Chaudhry;Muhammad Mohsin Riaz;Abdul Ghafoor; "A Framework for Outdoor RGB Image Enhancement and Dehazing," vol.15(6), pp.932-936, June 2018. A framework for image visibility restoration and haze removal is proposed. The proposed technique utilizes hybrid median filtering in conjunction with accelerated local Laplacian filtering for initial dehazing of images. For visual enhancement and correct restoration of colors, constrained <inline-formula> <tex-math notation="LaTeX">$l_{0}$ </tex-math></inline-formula>-based gradient image decomposition is applied. The proposed technique not only effectively removes haze from the images but also addresses the issues of distorted colors, visual, and halo artifacts, and haze removal from sky region in images in a better way when compared to other techniques. Experiments were performed on outdoor RGB images as well as remotely sensed images. The effectiveness of our proposed technique is demonstrated by quantitative and visual analyzes.

Wenchao Liu;Long Ma;He Chen; "Arbitrary-Oriented Ship Detection Framework in Optical Remote-Sensing Images," vol.15(6), pp.937-941, June 2018. Ship detection is a challenging problem in complex optical remote-sensing images. In this letter, an effective ship detection framework in remote-sensing images based on the convolutional neural network is proposed. The framework is designed to predict bounding box of ship with orientation angle information. Note that the angle information which is added to bounding box regression makes bounding box accurately fit into the ship region. In order to make the model adaptable to the detection of multiscale ship targets, especially small-sized ships, we design the network with feature maps from the layers of different depths. The whole detection pipeline is a single network and achieves real-time detection for a <inline-formula> <tex-math notation="LaTeX">$704 times 704$ </tex-math></inline-formula> image with the use of Titan X GPU acceleration. Through experiments, we validate the effectiveness, robustness, and accuracy of the proposed ship detection framework in complex remote-sensing scenes.

Amirabbas Davari;Erchan Aptoula;Berrin Yanikoglu;Andreas Maier;Christian Riess; "GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data," vol.15(6), pp.942-946, June 2018. The amount of training data that is required to train a classifier scales with the dimensionality of the feature data. In hyperspectral remote sensing (HSRS), feature data can potentially become very high dimensional. However, the amount of training data is oftentimes limited. Thus, one of the core challenges in HSRS is how to perform multiclass classification using only relatively few training data points. In this letter, we address this issue by enriching the feature matrix with synthetically generated sample points. These synthetic data are sampled from a Gaussian mixture model (GMM) fitted to each class of the limited training data. Although the true distribution of features may not be perfectly modeled by the fitted GMM, we demonstrate that a moderate augmentation by these synthetic samples can effectively replace a part of the missing training samples. Doing so, the median gain in classification performance is 5% on two datasets. This performance gain is stable for variations in the number of added samples, which makes it easy to apply this method to real-world applications.

Brittany Morago;Giang Bui;Truc Le;Norbert H. Maerz;Ye Duan; "Photograph LIDAR Registration Methodology for Rock Discontinuity Measurement," vol.15(6), pp.947-951, June 2018. Rock detachment events along roadways pose public safety concerns but can be predicted and safely handled using geological measurements of discontinuities. With modern sensing technology, these measurements can be taken on 3-D point clouds and 2-D optical images that provide a high level of structural accuracy and visual detail. Doing so allows engineers to obtain the needed data with relative ease while eliminating the biases and hazards inherent in taking manual measurements. This letter presents an approach for fusing the 2-D and 3-D data in natural and unstructured scenes. This includes a novel method for visualizing imagery obtained with very different sensors to maximize their visual similarity making registration a more tangible task. To show the effectiveness of our registration methodology, we evaluate measurements taken manually and digitally on rock facet and cut discontinuity orientations in Rolla, MO. Our method is able to align the 2-D and 3-D data with an accuracy of under 2 cm. The median difference between measurements manually obtained by a geological engineer and those obtained with our proposed software is 3.65.

Yihua Hu;Liren Guo;Xiao Dong;Shilong Xu; "Overlapping Laser Micro-Doppler Feature Extraction and Separation of Weak Vibration Targets," vol.15(6), pp.952-956, June 2018. The laser-detected micro-Doppler (MD) effect is more likely to achieve precision target identification and recognition because of its high-accuracy estimation ability. MD features overlapping in the time–frequency (TF) are encountered in targets with similar micromotion parameters, which cannot be solved with one-channel detection. In this letter, a novel separation method based on a constrained particle filter (PF) in a time-varying autoregressive (TVAR) model is developed to solve this extremely underdetermined problem. First, the TVAR model for a multicomponent MD signal is established, and the connection between the interested instantaneous frequency (IF) and model poles is analyzed. Then, the continuity characteristic of the IF law is used to design a constraint condition for a PF assuming that the laser MD effect obeys the sinusoidal frequency modulation form. By fusing the constraint into the process of particle update and weight computation, the IF curve for each component is correctly separated through the well-tracked pole trajectories. Finally, the performances of the presented method and traditional method are compared for a TF overlapping scenario. The simulation results verify the validity and necessity of the new method; meanwhile, the low-level computational complexity makes it possible for real-time processing.

Junshi Xia;Naoto Yokoya;Akira Iwasaki; "Fusion of Hyperspectral and LiDAR Data With a Novel Ensemble Classifier," vol.15(6), pp.957-961, June 2018. Due to the development of sensors and data acquisition technology, the fusion of features from multiple sensors is a very hot topic. In this letter, the use of morphological features to fuse a hyperspectral (HS) image and a light detection and ranging (LiDAR)-derived digital surface model (DSM) is exploited via an ensemble classifier. In each iteration, we first apply morphological openings and closings with a partial reconstruction on the first few principal components (PCs) of the HS and LiDAR data sets to produce morphological features to model spatial and elevation information for HS and LiDAR data sets. Second, three groups of features (i.e., spectral and morphological features of HS and LiDAR data) are split into several disjoint subsets. Third, data transformation is applied to each subset and the features extracted in each subset are stacked as the input of a random forest classifier. Three data transformation methods, including PC analysis, linearity preserving projection, and unsupervised graph fusion, are introduced into the ensemble classification process. Finally, we integrate the classification results achieved at each step by a majority vote. Experimental results on coregistered HS and LiDAR-derived DSM demonstrate the effectiveness and potentialities of the proposed ensemble classifier.

Rogério Galante Negri;Erivaldo Antônio da Silva;Wallace Casaca; "Inducing Contextual Classifications With Kernel Functions Into Support Vector Machines," vol.15(6), pp.962-966, June 2018. Kernel functions have revolutionized theory and practice in the field of pattern recognition, especially to perform image classification. Besides giving rise to nonlinear variants of the well-known support vector machine (SVM), these functions have also been successfully used to classify nonvectorial data (e.g., graphs and collection of sets), in which customized metrics are created to precisely measure the similarity among such contextual data entities. This letter introduces two context-inspired kernel functions as new SVM-driven methods for remote sensing image classification. In contrast to the existing SVM-based approaches that assume only multiattribute vectors as representative features in a high-dimensional space, the proposed models formally establish comparisons between the entire sets of context-given data, thus employing these contextual measurements to drive the classification. More precisely, stochastic distances as well as hypothesis tests are conveniently handled and “kernelized” to build our models. A complete battery of experiments involving both remote sensing and real-world images is conducted to validate the performance of the proposed kernels against various well-established SVM-based methods.

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Naoto Yokoya;Pedram Ghamisi;Junshi Xia;Sergey Sukhanov;Roel Heremans;Ivan Tankoyeu;Benjamin Bechtel;Bertrand Le Saux;Gabriele Moser;Devis Tuia; "Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest," vol.11(5), pp.1363-1377, May 2018. In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference.

Andrew T. Fontenot;Hesham Mohamed El-Askary;Michael J. Garay;James R. Campbell;Olga V. Kalashnikova; "Characterizing the Impact of Aerosols on Pre-Hurricane Sandy," vol.11(5), pp.1378-1386, May 2018. This study focuses on the role that African dust over the Atlantic had on the persistence of the tropical system that eventually became Hurricane Sandy in October 2012. On October 8, a Saharan dust event in the Mauritania region of West Africa transported significant amounts of mineral dust into the troposphere and along the path of an easterly wave created by a break in the Intertropical Convergence Zone (ITCZ). The Terra/Aqua-MODIS satellite observations clearly define the spatial distribution of the coarse/fine aerosols, while the CALIPSO observations of the total attenuated backscatter at 532 nm provide a detailed view of the vertical structure and aerosol types in the dust-laden layer. European Centre for Medium-Range Weather Forecasts and Modern-Era Retrospective Analysis for Research and Applications, Version 2 reanalysis data show the distribution of aerosols along the path of the pre-Sandy wave as well as a second wave that formed north of the ITCZ under different condition. The second wave, which started in an area of relatively larger aerosol optical depth (AOD), moved into an area with abnormally low convective available potential energy and AOD, subsequently dying out, while the wave that became Sandy had light aerosol loading (AOD between 0.15-0.5) along a majority of its path. The evidence suggests that aerosols played a nontrivial role in the maintenance of this system until it moved into an environment favorable for cyclogenesis.

Jing Lu;Li Jia;Massimo Menenti;Yuping Yan;Chaolei Zheng;Jie Zhou; "Performance of the Standardized Precipitation Index Based on the TMPA and CMORPH Precipitation Products for Drought Monitoring in China," vol.11(5), pp.1387-1396, May 2018. This paper evaluated the accuracy of multiple satellite-based precipitation products including the tropical rainfall measuring mission multisatellite precipitation analysis (TMPA) (TMPA 3B42RT and TMPA 3B42 version 7) and the Climate Prediction Center MORPHing technique (CMORPH) (CMORPH RAW and CMORPH BLD version 1.0) datasets and investigated the impact of the accuracy and temporal coverage of these data products on the reliability of the standardized precipitation index (SPI) estimates. The satellite-based SPI was compared with the SPI estimate using in situ precipitation observations from 2221 meteorological observation sites across China from 1998 to 2014. The SPI values calculated from the products calibrated with rain gauge measurements (TMPA 3B42 and CMORPH BLD) are generally more consistent with the SPI obtained with in situ measurements than those obtained using noncalibrated products (TMPA 3B42RT and CMORPH RAW products). The short data record of satellite precipitation data products is not the primary source of large errors in the SPI estimates, suggesting that the SPI estimate using satellite precipitation data products can be applied to drought assessment and monitoring. Satellite-based SPI estimates are more accurate in eastern China than in western China because of larger uncertainties in precipitation retrievals in western China, characterized by arid and semiarid climate conditions and complex landscapes. The satellite-based SPI can capture typical drought events throughout China, with the limitation that it is based on precipitation only and that different durations of antecedent precipitation are only suitable for specific drought conditions.

Lin Li;Qianguo Xing;Xuerong Li;Dingfeng Yu;Jun Zhang;Jingqiu Zou; "Assessment of the Impacts From the World's Largest Floating Macroalgae Blooms on the Water Clarity at the West Yellow Sea Using MODIS Data (2002–2016)," vol.11(5), pp.1397-1402, May 2018. Water clarity (Secchi disk depth, SDD) is a very important factor for marine ecological environment. The world's largest “green tide” caused by the macroalgal blooms (MABs) of green macroalgae has occurred every summer in the Yellow Sea since 2008. In this study, we first present the effects of MABs on the water clarity in the west Yellow Sea. A regional empirical retrieval algorithm of SDD on the basis of moderate resolution imaging spectroradiometer (MODIS) remote sensing reflectance is evaluated with the field data and satellite reflectance data: the spectral simulation with the end-member reflectance of sea water and macroalgae, and the MODIS Level-2 standard products of the remote sensing reflectance. The results show that the mixture of sea water and macroalgae will lead to decreased water clarity when the SDD is larger than 1.2 m and increased chlorophyll-a, i.e., false values in the standard products for pure sea water which therefore should be used with caution for the regions with large scale of floating macroalgae blooms. The long-term SDD in June and July (2002-2016) over the Yellow Sea is investigated and analyzed with the presence of “green tide.” The significant decrease in the SDD by 2.6 m and with 12 544 km2 of sea surface in total in July while no pronouncing changes in June suggests that the water clarity in the west Yellow Sea has been strongly affected from the period of 2002-2007 (the pre-MAB phase) to the period of 2008-2016 (the MAB phase).

Katalin Blix;Torbjørn Eltoft; "Evaluation of Feature Ranking and Regression Methods for Oceanic Chlorophyll-a Estimation," vol.11(5), pp.1403-1418, May 2018. This paper evaluates two alternative regression techniques for oceanic chlorophyll-a (Chl-a) content estimation. One of the investigated methodologies is the recently introduced Gaussian process regression (GPR) model. We explore two feature ranking methods derived for the GPR model, namely sensitivity analysis (SA) and automatic relevance determination (ARD). We also investigate a second regression method, the partial least squares regression (PLSR) for oceanic Chl-a content estimation. Feature relevance in the PLSR model can be accessed through the variable importance in projection (VIP) feature ranking algorithm. This paper thus analyzes three feature ranking models, SA, ARD, and VIP, which are all derived from different fundamental principles, and uses the ranked features as inputs to the GPR and PLSR to assess regression strengths. We compare the regression performances using some common performance measures, and show how the feature ranking methods can be used to find the lowest number of features to estimate oceanic Chl-a content by using the GPR and PLSR models, while still producing comparable performance to the state-of-the-art algorithms. We evaluate the models on a global MEdium Resolution Imaging Spectrometer matchup dataset. Our results show that the GPR model has the best regression performance for most of the input feature sets we used, and our conclusion is this model can favorably be used for Chl-a content retrieval, already with two features, ranked by either the SA or ARD methods.

Weikai Tan;Jonathan Li;Linlin Xu;Michael A. Chapman; "Semiautomated Segmentation of Sentinel-1 SAR Imagery for Mapping Sea Ice in Labrador Coast," vol.11(5), pp.1419-1432, May 2018. This study aims at proposing a semiautomated sea ice segmentation workflow utilizing Sentinel-1 synthetic aperture radar imagery. The workflow consists of two main steps. First, preferable features in sea ice interpretation were determined with a random forest feature selection method. Second, an unsupervised graph-cut image segmentation was performed. The workflow was tested on 13 Sentinel-1A images from January to June 2016, and the results were evaluated on open water segmentation per ice charts provided by Canada Ice Service. The results showed that the proposed workflow was able to segment Sentinel-1 images in to appropriate number of classes, and the potential water identification rate reached 95%.

Xiaoyan Wang;Jian Wang;Tao Che;Xiaodong Huang;Xiaohua Hao;Hongyi Li; "Snow Cover Mapping for Complex Mountainous Forested Environments Based on a Multi-Index Technique," vol.11(5), pp.1433-1441, May 2018. Seasonal snow cover is a critical component of the energy and water budgets of mountainous watersheds. Capturing the snow cover in complex environments is crucial for monitoring and understanding the temporal and spatial effects of climate change on alpine snow cover. The normalized difference snow index (NDSI) can be used to effectively and accurately estimate snow cover information from satellite images. However, the NDSI has limited utility for estimating the snow cover in heavily forested areas and relating this information to snowmelt-based runoff. In this study, a new algorithm based on a multi-index technique is proposed. The technique combines the NDSI, the normalized difference forest snow index, and the normalized difference vegetation index, and decision rules are established to increase the accuracy of snow mapping in forested areas. The new algorithm based on a multi-index technique is tested in the mountainous forested areas of North Xinjiang, China. In a winter image with full snow and a spring image with patchy snow, most of the forest snow, which is underestimated by the NDSI, is recognized by the multi-index technique. The accuracy of snow detection in forested areas is more than 90%. Additionally, in an experiment using a summer image without snow in forested areas no commission errors were detected. The snow detection algorithm based on a multi-index technique uses a simple set of decision rules for snow and can be run automatically without a priori knowledge of the surface characteristics.

Junshen Lu;Georg Heygster;Gunnar Spreen; "Atmospheric Correction of Sea Ice Concentration Retrieval for 89 GHz AMSR-E Observations," vol.11(5), pp.1442-1457, May 2018. An improved sea ice concentration (SIC) retrieval algorithm named ASI2 that uses weather corrected polarization difference (PD) of brightness temperatures (TBs) at 89 GHz measured by AMSR-E/2 is developed. Effects of wind, total water vapor, liquid water path, and surface temperature on the TBs are evaluated through a radiative transfer model. TBs of open ocean yield higher sensitivity to the atmospheric water due to its low emissivity, whereas that of sea ice is more influenced by the surface conditions such as temperature and ice type. The weather effects are corrected by simulating changes in TBs caused by the atmospheric water absorption/emission and wind roughened ocean surface using numerical weather prediction reanalysis data fields as atmospheric profiles. ASI2 is validated on a collection of AMSR-E observations over open water and 100% SIC. The correction significantly reduces the standard deviation and bias of SIC over open water, yet yields little change over 100% SIC. Combined with an improved weather filter based on the corrected TBs at lower frequencies, ASI2 allows retrieval of low ice concentration and resolves a more exact ice concentration gradient across the ice edge compared to the original ASI algorithm.

Huaqiang Du;Fangjie Mao;Xuejian Li;Guomo Zhou;Xiaojun Xu;Ning Han;Shaobo Sun;Guolong Gao;Lu Cui;Yangguang Li;Dien Zhu;Yuli Liu;Liang Chen;Weiliang Fan;Pingheng Li;Yongjun Shi;Yufeng Zhou; "Mapping Global Bamboo Forest Distribution Using Multisource Remote Sensing Data," vol.11(5), pp.1458-1471, May 2018. Bamboo forest has great potential in climate change mitigation. However, the spatiotemporal pattern of carbon storage of global bamboo forest is still cannot be accurately estimated, because the lack of an accurate global bamboo forest distribution information. In this paper, the global bamboo forest distribution was mapped with the following steps. To begin with, training samples were obtained based on investigation data, statistic data, and literature data. Then, a decision tree was constructed for mapping the global bamboo forest distribution by integrating Landsat 8 OLI and MODIS data. Finally, the global bamboo forest area was estimated using a pixel unmixing algorithm. The constructed decision tree succeeds in extracting global bamboo forest based on remote sensing data, where the overall accuracy of classification was 78.81%. The estimated total global bamboo forest area was 30538.35 × 103 ha, with a low root-mean-square error of 611.1 × 103 ha. The estimated bamboo forest area of each province in China and each country were high consistent with the National Forest Inventory in China and Food and Agriculture Organization of the United Nations statistic results (average R2 > 0.9), respectively. Therefore, the global bamboo forest map yielded a satisfactory accuracy in both classification and area estimation, and could provide accurate and significant support for global bamboo forest resource management and carbon cycle research.

Ling Wu;Xiangnan Liu;Qiming Qin;Bingyu Zhao;Yujia Ma;Mengxue Liu;Tian Jiang; "Scaling Correction of Remotely Sensed Leaf Area Index for Farmland Landscape Pattern With Multitype Spatial Heterogeneities Using Fractal Dimension and Contextural Parameters," vol.11(5), pp.1472-1481, May 2018. High-accuracy retrieval of the crop leaf area index (LAI) in farmlands via remote sensing is the premise of reflecting the true growth condition of the crop. This paper aimed at scaling correction of LAI retrieval and developed an LAI scaling transfer model for farmland landscape pattern with multitype spatial heterogeneities according to the multiple types of farmland underlying surfaces in China. The interclass heterogeneity (caused by the alternate distribution of different cover types) and intraclass heterogeneity (caused by the difference in growth conditions within the same crop) both exist in the farmland landscape. The contextural parameters (fractions of components) and fractal dimension of the up-scaling pixel were used to quantitatively describe and correct the scaling effect caused by the two types of spatial heterogeneity, respectively. A scaling transfer model of inversed LAI was built by comprehensively considering intraclass and interclass heterogeneities. Results indicated that the LAI scaling bias of the up-scaling mixed pixel was mainly caused by the interclass heterogeneity even when the areal proportion of the noncrop component was low. The scaling transfer model corrected the scaling effect of LAI, with the root-mean-square error and mean absolute percentage error decreasing from 0.599 and 10.00% to 0.077 and 1.11%, respectively. The developed method based on fractal theory and contextural parameters effectively weakened the influence of the scaling effect on the accuracy of LAI retrieval.

Qiaoyun Xie;Jadu Dash;Wenjiang Huang;Dailiang Peng;Qiming Qin;Hugh Mortimer;Raffaele Casa;Stefano Pignatti;Giovanni Laneve;Simone Pascucci;Yingying Dong;Huichun Ye; "Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval," vol.11(5), pp.1482-1493, May 2018. Leaf area index (LAI) is a crucial biophysical variable for agroecosystems monitoring. Conventional vegetation indices (VIs) based on red and near infrared regions of the electromagnetic spectrum, such as the normalized difference vegetation index (NDVI), are commonly used to estimate the LAI. However, these indices commonly saturate at moderate-to-dense canopies (e.g., NDVI saturates when LAI exceeds three). Modified VIs have then been proposed to replace the typical red/green spectral region with the red-edge spectral region. One significant and often ignored aspect of this modification is that the reflectance in the red-edge spectral region is comparatively sensitive to chlorophyll content which is highly variable between different crops and different phenological states. In this study, three improved indices are proposed combining reflectance both in the red and red-edge spectral regions into the NDVI, the modified simple ratio index (MSR), and the green chlorophyll index (<inline-formula><tex-math notation="LaTeX"> ${text{CI}}_{{text{green}}}$</tex-math></inline-formula>) formula. These improved indices are termed <inline-formula> <tex-math notation="LaTeX">${text{NDVI}}_{{text{red}& text{RE}}}$</tex-math></inline-formula> (red and red-edge NDVI), <inline-formula><tex-math notation="LaTeX">${text{MSR}}_{{text{red}& text{RE}}}$</tex-math> </inline-formula> (red and red-edge MSR index), and <inline-formula><tex-math notation="LaTeX"> ${text{CI}}_{{text{red}& text{RE}}}$</tex-math></inline-formula> (red and red-edge CI). The indices were tested using RapidEye images and in-situ data from campaigns at Maccarese Farm (Central Rome, Italy), in which four crop types at four different growth stages were measured. We investigated the predictive power of nine VIs for crop LAI estimation, including NDVI, MSR, and <inline-formula><tex-math notation="LaTeX"> ${text{CI}}_{{text{green}}}$</tex-math></inline-formula>; the red-edge - odified indices: <inline-formula> <tex-math notation="LaTeX">${text{NDVI}}_{{text{Red-edge}}}$</tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${text{MSR}}_{{text{Red-edge}}}$</tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">${text{CI}}_{{text{Red-edge}}}$</tex-math></inline-formula> (generally represented by <inline-formula><tex-math notation="LaTeX">${text{VI}}_{{text{Red-edge}}}$</tex-math></inline-formula>); and the newly improved indices: <inline-formula><tex-math notation="LaTeX">${text{NDVI}}_{{text{red}& text{RE}}}$ </tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">${text{MSR}}_{{text{red}& text{RE}}}$ </tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">${text{CI}}_{{text{red}& text{RE}}}$</tex-math></inline-formula> (generally represented by VI<inline-formula><tex-math notation="LaTeX"> ${}_{text{red}& text{RE}}$</tex-math></inline-formula>). The results show that <inline-formula> <tex-math notation="LaTeX">${text{VI}}_{{text{red}& text{RE}}}$</tex-math></inline-formula> improves the coefficient of determination (R2) for LAI estimation by 10% in comparison to <inline-formula><tex-math notation="LaTeX">${text{VI}}_{{text{Red-edge}}}$</tex-math></inline-formula>. The newly improved indices prove to be the powerful alternatives for the LAI estimation of crops with wide chlorophyll range, and may provide valuable information for satellites equipped with red-edge channels (such as Sentinel-2) when applied to precision agriculture.

Virginia Brancato;Irena Hajnsek; "Analyzing the Influence of Wet Biomass Changes in Polarimetric Differential SAR Interferometry at L-Band," vol.11(5), pp.1494-1508, May 2018. The displacement estimated with differential SAR interferometry (DInSAR) might not be unique when more than one polarization channel is available. For the case of agricultural areas, these ambiguities have been mainly related to complex vegetation dynamics, i.e., vegetation growth. This study intends to explore the potential of a synergistic use of DInSAR with SAR Polarimetry (PolDInSAR) in tracking changes within agricultural vegetation covers. The connection between the PolDInSAR observables (i.e., herein, the DInSAR phases at various polarization channels and/or their linear combinations) with wet biomass and soil water content changes is empirically investigated with linear regression techniques. This is done in the frame of an L-band airborne DInSAR dataset. The impact of vegetation vigor differs depending on the type of crop analyzed. For those crops exhibiting a birefringent electromagnetic propagation (i.e., barley, wheat, and rapeseed), the influence of wet biomass is particularly pronounced in the VV DInSAR phase but also in the HH-VV phase difference. Contrarily to the former, the latter shows also a scarce sensitivity to changes in soil water content. Therefore, this PolDInSAR observable is used to generate biomass maps of the analyzed test site. The predicted biomass variations are in good agreement with the collected in situ measurements, i.e., the coefficient of determination varies between 0.8 and 0.9.

Yuanyuan Liu;Chaoying Zhao;Qin Zhang;Chengsheng Yang;Jing Zhang; "Land Subsidence in Taiyuan, China, Monitored by InSAR Technique With Multisensor SAR Datasets From 1992 to 2015," vol.11(5), pp.1509-1519, May 2018. Taiyuan city has been suffering significant subsidence during last two to three decades, mainly due to the effects of groundwater withdrawal and urban construction. The purpose of this study is to map the spatial-temporal variations of land subsidence over Taiyuan and analyze the causes of the observed deformations by using the interferometric point target analysis (IPTA) technique with multisensor SAR datasets during 1992 and 2015. The InSAR-derived deformations are then compared to the leveling measurements and groundwater data. The observed deformation based on ERS-1 datasets has mapped regional subsidence rate ranging from 30 to 60 mm/a in the northern and central Taiyuan from 1992 to 1993. InSAR measurements from Envisat ASAR, TerraSAR-X, and Radarsat-2 data reveal land subsidence rate up to 80 mm/a in the southern suburb during 2009 to 2015, whereas a rebound rate more than 10 mm/a in northern Taiyuan from 2004 to 2005. The time series deformation maps from 2009 to 2010 present slight nonlinear periodic variations, which might be caused by the seasonal groundwater fluctuations. The observed InSAR results indicate that the pattern of ground deformation is nearly concentric around locations of intense groundwater withdrawal, and the spatial extent of the subsiding area has been shrinking and moving toward the central and southern Taiyuan after 2003. Furthermore, differential subsidence rates are identified on both sides of Tianzhuang fault from observed deformation maps during the period of 2009-2010, 2012-2013, and 2014-2015, which could be explained that the fault acts as the barrier to the groundwater flow. Our results could provide significant evidence to serve the decision-making on land subsidence mitigation in Taiyuan, China.

Zhi Yong Lv;Wenzhong Shi;Xiaokang Zhang;Jón Atli Benediktsson; "Landslide Inventory Mapping From Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation," vol.11(5), pp.1520-1532, May 2018. Landslide inventory mapping (LIM) plays an important role in hazard assessment and hazard relief. Even though much research has taken place in past decades, there is space for improvements in accuracy and the usability of mapping systems. In this paper, a new landslide inventory mapping framework is proposed based on the integration of the majority voting method and the multiscale segmentation of a postevent images, making use of spatial feature of landslide. Compared with some similar state-of-the-art methods, the proposed framework has three advantages: 1) the generation of LIM is almost automatic; 2) the framework can achieve more accurate results because it takes into account the landslide spatial information in an irregular manner through multisegmentation of the postevent image and object-based majority voting (MV); and 3) it needs less parameter tuning. The proposed framework was applied to four landslide sites on Lantau Island, Hong Kong. Compared with existing methods, including region level set evolution (RLSE), edge level set evolution (ELSE) and change detection Markov random field (CDMRF) methods, quantitative evaluation shows the proposed framework is competitive in terms of Completeness. The framework outperformed RLSE, ELSE, and CDMRF for the four experiments by more than 9% in Correctness and by 8% in Quality. To the authors' knowledge, this is the first-time that landslide spatial information has been utilized through the integration of multiscale segmentation of postevent image with the MV approach to obtain LIM using high spatial resolution remote sensing images. The approach is also of wide generality and applicable to other kinds of land cover change detection using remote sensing images.

Manuel Titos;Angel Bueno;Luz García;Carmen Benítez; "A Deep Neural Networks Approach to Automatic Recognition Systems for Volcano-Seismic Events," vol.11(5), pp.1533-1544, May 2018. Deep neural networks (DNNs) could help to identify the internal sources of volcano-seismic events. However, direct applications of DNNs are challenging, given the multiple seismic sources and the small size of available datasets. In this paper, we propose a novel approach in the field of volcano seismology to classify volcano-seismic events based on fully connected DNNs. Two DNN architectures with different weights scheme initialization are studied: stacked denoising autoencoders and deep belief networks. Using a combined feature vector of linear prediction coefficients and statistical properties, we evaluate classification performance on seven different classes of isolated seismic events. These proposed architectures are compared to multilayer perceptron, support vector machine, and random forest. Experimental results show that DNNs can efficiently capture complex relationships of volcano-seismic data and achieve better classification performance with faster convergence when compared to classical models.

Haining Yang;Na Li;Tingjun Li;Qing Huo Liu; "Least-Square-Based Nonuniform Borehole SAR Imaging for Subsurface Sensing," vol.11(5), pp.1545-1555, May 2018. This paper presents the least-square-based nonuniform borehole synthetic aperture radar (SAR) imaging method with cosine accuracy factor for subsurface sensing. Based on the Stolt migration, the frequency-wavenumber spectrum of nonuniform data is efficiently approximated in the least-square-sense for the target space generation. The nonuniform power exponent basis is interpolated into several uniform power exponent bases with cosine accuracy factors, and then a virtual uniform sample set with a larger scale is generated for frequency-wavenumber spectrum approximation and imaging process. The proposed method can give accurate subsurface image result with nonuniform data at a greatly reduced computational cost. The approximation error and computational cost of the proposed method are analyzed and compared with those of Gaussian nonuniform imaging method. The imaging capabilities of the proposed method are theoretically simulated and experimentally demonstrated for distributed targets. The results show that the normalized mean square error and normalized maximum error of the proposed method are at least 8.07 dB and 4.29 dB, respectively, lower than those of conventional Stolt migration method. The imaging properties of this proposed method are shown to be superior to the conventional Stolt migration method, Gaussian nonuniform imaging method and Kirchhoff migration method, which is suitable for large nonuniform SAR imaging applications.

Donato Amitrano;Francesca Cecinati;Gerardo Di Martino;Antonio Iodice;Pierre-Philippe Mathieu;Daniele Riccio;Giuseppe Ruello; "Feature Extraction From Multitemporal SAR Images Using Selforganizing Map Clustering and Object-Based Image Analysis," vol.11(5), pp.1556-1570, May 2018. We introduce a new architecture for feature extraction from multitemporal synthetic aperture radar (SAR) data. Its the purpose is to combine classic SAR processing and geographical object-based image analysis to provide a robust unsupervised tool for information extraction from time series images. The architecture takes advantage from the characteristics of the recently introduced RGB products of the Level-1<inline-formula><tex-math notation="LaTeX"> $alpha$</tex-math></inline-formula> and Level-1<inline-formula><tex-math notation="LaTeX">$beta$</tex-math> </inline-formula> families, and employs self-organizing map clustering and object-based image analysis. In particular, the input products are clustered using color homogeneity and automatically enriched with a semantic attribute referring to clusters’ color, providing a preclassification mask. Then, in the frame of an application-oriented object-based image analysis, opportune layers measuring scattering and geometric properties of candidate objects are evaluated, and an appropriate rule-set is implemented in a fuzzy system to extract the feature of interest. The obtained results have been compared with those given by existing techniques and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semiarid environment.

Corneliu Octavian Dumitru;Gottfried Schwarz;Mihai Datcu; "SAR Image Land Cover Datasets for Classification Benchmarking of Temporal Changes," vol.11(5), pp.1571-1592, May 2018. The increased availability of high-resolution synthetic aperture radar (SAR) satellite images has led to new civil applications of these data. Among them is the systematic classification of land cover types based on the patterns of settlements or agriculture recorded by SAR imagers, in particular the identification and quantification of temporal changes. A systematic (re)classification shall allow the assignment of continuously updated semantic content labels to local image patches. This necessitates a careful selection of well-defined and discernible categories being contained in the image data that have to be trained and validated. These steps are well-established for optical images, while the peculiar imaging characteristics of SAR sensors often prevent a comparable approach. Especially, the vast range of SAR imaging parameters and the diversity of local targets impact the image product characteristics and need special care. In the following, we present guidelines and practical examples of how to obtain reliable image patch classification results for time series data with a limited number of given training data. We demonstrate that one can avoid the generation of simulated training data if we decompose the classification task into physically meaningful subsets of characteristic target properties and important imaging parameters. Then, the results obtained during training can serve as benchmarking figures for subsequent image classification. This holds for typical remote sensing examples such as coastal monitoring or the characterization of urban areas where we want to understand the transitions between individual land cover categories. For this purpose, a representative dataset can be obtained from the authors. A final proof of our concept is the comparison of classification results of selected target areas obtained by rather different SAR instruments. Despite the instrumental differences, the final results are surprisingly similar.

Ling Chang;Rolf P. B. J. Dollevoet;Ramon F. Hanssen; "Monitoring Line-Infrastructure With Multisensor SAR Interferometry: Products and Performance Assessment Metrics," vol.11(5), pp.1593-1605, May 2018. Satellite radar interferometry (InSAR) is an emerging technique to monitor the stability and health of line-infrastructure assets, such as railways, dams, and pipelines. However, InSAR is an opportunistic approach as the location and occurrence of its measurements (coherent scatterers) cannot be guaranteed, and the quality of the InSAR products is not uniform. This is a problem for operational asset managers, who are used to surveying techniques that provide results with uniform quality at predefined locations. Therefore, advanced integrated products and generic performance assessment metrics are necessary. Here, we propose several new monitoring products and quality metrics for a-priori and a-posteriori performance assessment using multisensor InSAR. These products and metrics are demonstrated on a 125 km railway line-infrastructure asset in the Netherlands.

Xiangli Yang;Wen Yang;Hui Song;Pingping Huang; "Polarimetric SAR Image Classification Using Geodesic Distances and Composite Kernels," vol.11(5), pp.1606-1614, May 2018. The covariance/coherence matrices are the most common way of representing polarimetric information in the polarimetric synthetic aperture radar (PolSAR) data and have been extensively used in PolSAR classification. Since PolSAR covariance and coherence matrices are Hermitian positive-definite, they form a nonlinear manifold, rather than Euclidean space. Though the geodesic distance measures defined on a manifold are suitable for describing similarities of PolSAR matrix data, the nonlinearity of the manifold often makes the involved optimization problems awkward. To address this problem, we propose to embed the manifold-based PolSAR data into a high (infinite)-dimensional reproducing kernel Hilbert space by Stein kernel and log-Euclidean kernel. Besides, we introduce the composite kernel into the sparse representation classification in order to exploit the spatial context information of PolSAR data. The proposed method is assessed using different PolSAR datasets. Experimental results demonstrate the superior performance compared with the methods without the use of contextual information.

Xiaomei Luo;Xiangfeng Wang;Yuhao Wang;Shengqi Zhu; "Efficient InSAR Phase Noise Reduction via Compressive Sensing in the Complex Domain," vol.11(5), pp.1615-1632, May 2018. Two novel phase noise filtering algorithms for interferometric synthetic aperture radar (InSAR) are presented in this paper. Aiming at the nonlocal high self-similarity existing in the InSAR phase, we establish the phase noise filtering formulations with the ℓ0-norm regularizer and the ℓ1-norm regularizer, respectively. Although these two original formulations are nonconvex, we attempt to solve them by successive upper bound minimization combined with dictionary learning method. Specifically, for the noise reduction formulation with the ℓ0-norm regularizer, we first divide the original problem into a series of decoupled subproblems. Second, we obtain the approximate subproblem, which is locally tight upper bound of each subproblem by using a majorization- minimization technique. Third, we compute the sparse parameter vector for each approximate subproblem, followed by a matrix form update for the dictionary. The three steps are tackled cyclically until a satisfying solution is attained. The noise reduction problem with the ℓ1-norm regularizer is handled in a similar approach. We also establish the computational complexities of these two methods and summarize their distinct performance. Simulation results based on both synthetic data and simulated InAR data show that these two new InSAR phase noise reduction methods have much better performance than several existing phase filtering methods.

Guanzhou Chen;Xiaodong Zhang;Qing Wang;Fan Dai;Yuanfu Gong;Kun Zhu; "Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images," vol.11(5), pp.1633-1644, May 2018. Semantic segmentation has emerged as a mainstream method in very-high-resolution remote sensing land-use/land-cover applications. In this paper, we first review the state-of-the-art semantic segmentation models in both computer vision and remote sensing fields. Subsequently, we introduce two semantic segmentation frameworks: SNFCN and SDFCN, both of which contain deep fully convolutional networks with shortcut blocks. We adopt an overlay strategy as the postprocessing method. Based on our frameworks, we conducted experiments on two online ISPRS datasets: Vaihingen and Potsdam. The results indicate that our frameworks achieve higher overall accuracy than the classic FCN-8s and SegNet models. In addition, our postprocessing method can increase the overall accuracy by about 1%-2% and help to eliminate “salt and pepper” phenomena and block effects.

Manjun Qin;Fengying Xie;Wei Li;Zhenwei Shi;Haopeng Zhang; "Dehazing for Multispectral Remote Sensing Images Based on a Convolutional Neural Network With the Residual Architecture," vol.11(5), pp.1645-1655, May 2018. Multispectral remote sensing images are often contaminated by haze, which causes low image quality. In this paper, a novel dehazing method based on a deep convolutional neural network (CNN) with the residual structure is proposed for multispectral remote sensing images. First, multiple CNN individuals with the residual structure are connected in parallel and each individual is used to learn a regression from the hazy image to the clear image. Then, the outputs of CNN individuals are fused with weight maps to produce the final dehazing result. In the designed network, the CNN individuals, mining multiscale haze features through multiscale convolutions, are trained using different levels of haze samples to achieve different dehazing abilities. In addition, the weight maps change with the haze distribution, and the fusion of the CNN individuals is adaptive. The designed network is end-to-end, and putting a hazy image into it, the clear scene can be restored. To train the network, a wavelength-dependent haze simulation method is proposed to generate labeled data, which can synthesize hazy multispectral images highly close to real conditions. Experimental results show that the proposed method can accurately remove the haze in each band of multispectral images under different scenes.

Zhenfeng Shao;Jiajun Cai; "Remote Sensing Image Fusion With Deep Convolutional Neural Network," vol.11(5), pp.1656-1669, May 2018. Remote sensing images with different spatial and spectral resolution, such as panchromatic (PAN) images and multispectral (MS) images, can be captured by many earth-observing satellites. Normally, PAN images possess high spatial resolution but low spectral resolution, while MS images have high spectral resolution with low spatial resolution. In order to integrate spatial and spectral information contained in the PAN and MS images, image fusion techniques are commonly adopted to generate remote sensing images at both high spatial and spectral resolution. In this study, based on the deep convolutional neural network, a remote sensing image fusion method that can adequately extract spectral and spatial features from source images is proposed. The major innovation of this study is that the proposed fusion method contains a two branches network with the deeper structure which can capture salient features of the MS and PAN images separately. Besides, the residual learning is adopted in our network to thoroughly study the relationship between the high- and low-resolution MS images. The proposed method mainly consists of two procedures. First, spatial and spectral features are respectively extracted from the MS and PAN images by convolutional layers with different depth. Second, the feature fusion procedure utilizes the extracted features from the former step to yield fused images. By evaluating the performance on the QuickBird and Gaofen-1 images, our proposed method provides better results compared with other classical methods.

Mi Wang;Yufeng Cheng;Yuan Tian;Luxiao He;Yanli Wang; "A New On-Orbit Geometric Self-Calibration Approach for the High-Resolution Geostationary Optical Satellite GaoFen4," vol.11(5), pp.1670-1683, May 2018. With the successful launch of GaoFen4 (GF4), on-orbit high accuracy geometric calibration for the high-resolution geostationary optical satellite will be a new research topic. With the improvement in the geometric resolution from geostationary orbit, it will become more and more difficult to meet the requirements of both high geometric resolution and large coverage for the available reference data. The purpose of this paper is to explore a new self-calibration mode for GF4 and future high-resolution geostationary optical area array cameras based on the fewest ground control points (GCPs). To overcome the problems of overparameterization, strong correlation and lower significance of the traditional rigorous imaging model, the simplified physical internal model is proposed, and its effectiveness in describing and compensating for the camera internal distortion is verified. Based on the simplified physical internal model, the self-calibration method based on two GCPs and evenly distributed tie points of two images is proposed for the high accuracy estimation of the calibration parameters. The GCPs can be used to provide the absolute geographical constraints for scale information, and the tie points can be used to provide the global constraints for optimum estimation. After calibration, the internal distortion is well compensated, and the positioning accuracy with relatively few GCPs is shown to be better than 1.0 pixels for both the panchromatic and near-infrared sensor and the intermediate infrared sensor. This paper will provide a new usable concept and approach for the future higher resolution geostationary area array optical camera to overcome the stringent requirements of both high resolution and a large area of reference data for the traditional calibration method.

Chunli Dai;Ian M. Howat; "Detection of Saturation in High-Resolution Pushbroom Satellite Imagery," vol.11(5), pp.1684-1693, May 2018. Over the last decade, DigitalGlobe has launched a series of commercial Earth imaging satellites. These high-resolution satellite imageries provide an increasingly abundant data source for remote mapping of the Earth surface and its temporal variability. Among the factors affecting image quality is saturation of the charge-coupled device due to improper setting of the time delay integration level for the imaged surface, which results in along-track striping over areas of high radiance. We present and demonstrate an algorithm for the local detection of saturation striping by a wavelet transform, used to detect periodic variations of brightness (i.e., striping) with varying frequencies at different locations, combined with the use of unidirectional brightness gradients. The algorithm is applicable to raw, orthorectified, and resampled imagery. We test the algorithm using panchromatic images acquired by the GeoEye-1 and WorldView 1-3 sensors over polar regions. Saturation area classification masks generated by the algorithm agree well with manually identified areas of saturation. Manual validation of the algorithm applied to over 6000 images in Iceland reveals a high (>80%) success rate when the saturation levels are 2% or higher. Our general methodology may be widely applicable to periodic noise detection in imagery.

Radhika Ravi;Yun-Jou Lin;Magdy Elbahnasawy;Tamer Shamseldin;Ayman Habib; "Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform," vol.11(5), pp.1694-1714, May 2018. Mobile light detection and ranging (LiDAR) systems are widely used to generate precise 3-D spatial information, which in turn aids a variety of applications such as digital building model generation, transportation corridor asset management, telecommunications, precision agriculture, and infrastructure monitoring. Integrating such systems with one or more cameras would allow forward and backward projection between imagery and LiDAR data, thus facilitating several other high-level data processing activities, such as reliable feature extraction and colorization of point cloudsv. To increase the registration accuracy of point clouds derived from LiDAR data and imagery, along with their accuracy with respect to the ground truth, a simultaneous estimation of the mounting parameters relating the different laser scanners and cameras to the onboard global navigation satellite system (GNSS)/inertial navigation system (INS) unit is vital. This paper proposes a calibration procedure that directly estimates the mounting parameters for several spinning multibeam laser scanners and cameras onboard an airborne or terrestrial mobile platform. This procedure is based on the use of conjugate points and linear/planar features in overlapping images and point clouds derived from different drive-runs/flight lines. In order to increase the efficiency of semi-automatic conjugate feature extraction from the LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within an outdoor environment are used in this study. An experimental setup is used to evaluate the performance of the proposed calibration procedure for both airborne and terrestrial mobile mapping systems through the a posteriori variance factor of the least squares adjustment procedure and the quality of fit of the adjusted LiDAR point cloud and image points to linear/planar surfaces before and after the calibration process.

Haiyan Guan;Wanqian Yan;Yongtao Yu;Liang Zhong;Dilong Li; "Robust Traffic-Sign Detection and Classification Using Mobile LiDAR Data With Digital Images," vol.11(5), pp.1715-1724, May 2018. This study aims at building a robust method for detecting and classifying traffic signs from mobile LiDAR point clouds and digital images. First, this method detects traffic signs from mobile LiDAR point clouds with regard to a prior knowledge of road width, pole height, reflectance, geometrical structure, and traffic-sign size. Then, traffic-sign images are segmented by projecting the detected traffic-sign points onto the digital images. Afterward, the segmented traffic-sign images are normalized for automatic classification with a given image size. Finally, a traffic-sign classifier is proposed based on a supervised Gaussian-Bernoulli deep Boltzmann machine model. We evaluated the proposed method using datasets acquired by a RIEGL VMX-450 system. The traffic-sign detection accuracy of 86.8% was achieved; through parameter sensitivity analysis, the overall performance of traffic-sign classification achieved a recognition rate of 93.3%. The computational performance showed that our method provides a promising solution to traffic-sign detection and classification using mobile LiDAR point clouds and digital images.

Elvira Musicò;Claudio Cesaroni;Luca Spogli;John Peter Merryman Boncori;Giorgiana De Franceschi;Roberto Seu; "The Total Electron Content From InSAR and GNSS: A Midlatitude Study," vol.11(5), pp.1725-1733, May 2018. The total electron content (TEC) measured from the interferometric synthetic aperture radar (InSAR) and from a dense network of global navigation satellite system (GNSS) receivers are used to assess the capability of InSAR to retrieve ionospheric information, when the tropospheric contribution to the interferometric phase is reasonably negligible. With this aim, we select three night-time case studies over Italy and investigate the correlation between TEC from advanced land observing satellite-phased array type L-band synthetic aperture radar (ALOS-PALSAR) and from the Rete Integrata Nazionale GPS (RING) network, the latter considered as the reference true ionospheric TEC. To retrieve the TEC variability from ALOS-PALSAR, we first investigate the correlation between the integral of the azimuth shifts and the interferometric phase in the absence of ground motions (e.g., earthquakes) and/or heavy rain events. If correlation exists (as in two out of three case studies under investigation), we can assume the tropospheric contribution to the interferometric phase as negligible and the TEC variability from L-band InSAR can be retrieved. For these two case studies, the comparison between the TEC from the InSAR images and from the RING network is quite encouraging as the correlation coefficient is R ~ 0.67 in the first case and R ~ 0.83 in the second case. This result highlights the potential to combine InSAR and GNSS experimental measurements to investigate small-scale spatial variability of TEC, in particular over regions scarcely covered by ground-based GNSS receivers.

Ayman Habib;Tian Zhou;Ali Masjedi;Zhou Zhang;John Evan Flatt;Melba Crawford; "Boresight Calibration of GNSS/INS-Assisted Push-Broom Hyperspectral Scanners on UAV Platforms," vol.11(5), pp.1734-1749, May 2018. Low-cost unmanned aerial vehicles (UAVs) utilizing push-broom hyperspectral scanners are poised to become a popular alternative to conventional remote sensing platforms such as manned aircraft and satellites. In order to employ this emerging technology in fields such as high-throughput phenotyping and precision agriculture, direct georeferencing of hyperspectral data using onboard integrated global navigation satellite systems (GNSSs) and inertial navigation systems (INSs) is required. Directly deriving the scanner position and orientation requires the spatial and rotational relationship between the coordinate systems of the GNSS/INS and hyperspectral scanner to be measured. The spatial offset (lever arm) between the scanner and GNSS/INS unit can be measured manually. However, the angular relationship (boresight angles) between the scanner and GNSS/INS coordinate systems, which is more critical for accurate generation of georeferenced products, is difficult to establish. This paper presents three calibration approaches to estimate the boresight angles relating hyperspectral push-broom scanner and GNSS/INS coordinate systems. For reliable/practical estimation of the boresight angles, this paper starts with establishing the optimal/minimal flight and control/tie point configuration through a bias impact analysis starting from the point positioning equation. Then, an approximate calibration procedure utilizing tie points in overlapping scenes is presented after making some assumptions about the flight trajectory and topography of covered terrain. Next, two rigorous approaches are introduced - one using ground control points and other using tie features. The approximate/rigorous approaches are based on enforcing the collinearity and coplanarity of the light rays connecting the perspective centers of the imaging scanner, object point, and the respective image points. To evaluate the accuracy of the proposed approaches, estimated boresight angles are used for orthorectif- cation of six hyperspectral UAV dataset acquired over an agricultural field. Qualitative and quantitative evaluations of the results have shown significant improvement in the derived orthophotos to a level equivalent to the ground sampling distance of the used scanner (namely, 3-5 cm when flying at 60 m).

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* "Institutional Listings," vol.11(5), pp.C4-C4, May 2018.* Presents a listing of institutional institutions relevant for this issue of the publication.

IEEE Geoscience and Remote Sensing Magazine - new TOC (2018 May 24) [Website]

* "[Front cover[Name:_blank]]," vol.6(1), pp.C1-C1, March 2018.* Presents the front cover for this issue of the publication.

* "Call for nominations: for the GRSS Administrative Committee," vol.6(1), pp.C2-C2, March 2018.* The IEEE Geocience and Remote Sensing Society (GRSS) Nominations Committee calls upon our membership to nominate members to serve on the GRSS Administrative Committee (AdCom). A nominating petition carrying a minimum of 2% of the names of eligible Society members (~70), excluding students, shall automatically place that nominee on the slate. Such nominations must be made by 30 April 2018. The Nominations Committee may choose to include a name on the slate regardless of the number of names generated by the nominating petition process. Prior to submission of a nomination petition, the petitioner shall have determined that the nominee named in the petition is willing to serve if elected; and evidence of such willingness to serve shall be submitted with the petition. Candidates must be current Members of the IEEE and the GRSS. Petition signatures can be submitted electronically through the Society website or by signing, scanning, and electronically mailing the pdf file of the paper petition. The name of each member signing the paper petition shall be clearly printed or typed. For identification purposes of signatures on paper petitions, membership numbers and addresses as listed in the official IEEE Membership records shall be included. Only signatures submitted electronically through the Society website or original signatures on paper petitions shall be accepted. Further information is provided here.

* "Table of Contents," vol.6(1), pp.1-2, March 2018.* Presents the table of contents for this issue of the publication.

* "Staff Listing," vol.6(1), pp.2-2, March 2018.* Provides a listing of current staff, committee members and society officers.

James L. Garrison; "Welcome from the New Editor-in-Chief [From the Editor[Name:_blank]]," vol.6(1), pp.3-3, March 2018. Presents the introductory editorial for this issue of the publication.

Adriano Camps; "Greetings from Barcelona! [Presdient's Message[Name:_blank]]," vol.6(1), pp.4-6, March 2018. Presents the President’s message for this issue of the publication.

* "BGDDS 2018," vol.6(1), pp.6-6, March 2018.* Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

* "CISS 2018," vol.6(1), pp.6-6, March 2018.* Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

* "Call for papers," vol.6(1), pp.7-7, March 2018.* Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Fabio Dell'Acqua;Giuseppe Siciliano; "Technical Education on Aerospace and Remote Sensing: A Brief Global Overview," vol.6(1), pp.8-14, March 2018. The Lombardy Aerospace Industry Cluster [1] was founded in Italy in 2014 as the final step in constructing a representative institution for the regional system of aerospace industries. The process was initiated in 2009 with the first formal contacts between the local industry federation and the regional government of Lombardy, which led to the foundation of a provisional body (Distretto Aerospaziale Lombardo) in preparation for a more formalized industry cluster to come and in light of a mandate to represent the local aerospace industry politically.

Wei Li;Fubiao Feng;Hengchao Li;Qian Du; "Discriminant Analysis-Based Dimension Reduction for Hyperspectral Image Classification: A Survey of the Most Recent Advances and an Experimental Comparison of Different Techniques," vol.6(1), pp.15-34, March 2018. Hyperspectral imagery contains hundreds of contiguous bands with a wealth of spectral signatures, making it possible to distinguish materials through subtle spectral discrepancies. Because these spectral bands are highly correlated, dimensionality reduction, as the name suggests, seeks to reduce data dimensionality without losing desirable information. This article reviews discriminant analysisbased dimensionality-reduction approaches for hyperspectral imagery, including typical linear discriminant analysis (LDA), state-of-the-art sparse graph-based discriminant analysis (SGDA), and their extensions.

Silvia Mari;Giovanni Valentini;Stefano Serva;Tiziana Scopa;Mauro Cardone;Luca Fasano;Giuseppe Francesco De Luca; "COSMO-SkyMed Second Generation System Access Portfolio," vol.6(1), pp.35-43, March 2018. The Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO)-SkyMed Second Generation (CSG) ground segment (GS) is based on an interoperable and multimission design that provides CSG functionalities to external partners and access through the CSG to services belonging to other Earth observation (EO) partners. Moreover, the CSG GS design supports such cooperation by expansion through replication of user GSs (UGSs) in different ways. In this manner, the CSG GS is able to manage EO foreign missions by providing centralized and multimission access in an integrated environment, thus offering valuable technological solutions to the defense and civilian communities. This article provides an indepth description of the CSG system access portfolio, focusing on the architectural details of the GS that allow the provisioning and exploitation of the CSG's interoperability, expandability, and multisensor/multimission (IEM) features.

Jorge L. Marquez;Carlos Marcelo Scavuzzo; "Activities of the GRSS Argentine Section Chapter [Chapters[Name:_blank]]," vol.6(1), pp.44-46, March 2018. Provides several short items that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Zhuosen Wang;Xiaofeng Li;Eugene Genong Yu;James C. Tilton; "Activities of the GRSS Washington/Northern Virginia Chapter [Chapters[Name:_blank]]," vol.6(1), pp.47-48, March 2018. Provides several short items that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Linda Hayden; "GLOBE Data Entry App Version 1.3 Now Available: Create and Edit Sites Without Active Internet Connection [Education[Name:_blank]]," vol.6(1), pp.49-51, March 2018. The Global Learning and Observations to Benefit the Environment (GLOBE) program is a worldwide hands-on primary and secondary school-based science and education program. GLOBE’s vision promotes and supports collaboration among students, teachers, and scientists on inquiry-based investigations of the environment and the Earth system, working in close partnership with NASA, the National Oceanic and Atmospheric Administration (NOAA), and the National Science Foundation (NSF) Earth System Science Projects for study and research about the dynamics of Earth’s environment.

* "PIERS 2018," vol.6(1), pp.51-51, March 2018.* Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

Bertrand Le Saux;Naoto Yokoya;Ronny Hansch;Saurabh Prasad; "2018 IEEE GRSS Data Fusion Contest: Multimodal Land Use Classification [Technical Committees[Name:_blank]]," vol.6(1), pp.52-54, March 2018. Presents information on the 2018 IEEE GRSS Data Fusion Contest.

* "[Calendar[Name:_blank]]," vol.6(1), pp.55-55, March 2018.* Provides a listing of upcoming events of interest to practitioners and researchers.

* "Remote Sensing Code Library (RSCL)," vol.6(1), pp.C3-C3, March 2018.* RSCL is a publication of IEEE GRSS, just like the GRSS Transactions and the GRSS Newsletter, but its currency is computer codes associated with one or more aspect of geoscience remote sensing. For more information, contact the RSCL Editor.

Topic revision: r6 - 22 May 2015, AndreaVaccari
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