Relevant TOCs

IEEE Transactions on Image Processing - new TOC (2017 June 19) [Website]

Huan Lei;Guang Jiang;Long Quan; "Fast Descriptors and Correspondence Propagation for Robust Global Point Cloud Registration," vol.26(8), pp.3614-3623, Aug. 2017. In this paper, we present a robust global approach for point cloud registration from uniformly sampled points. Based on eigenvalues and normals computed from multiple scales, we design fast descriptors to extract local structures of these points. The eigenvalue-based descriptor is effective at finding seed matches with low precision using nearest neighbor search. Generally, recovering the transformation from matches with low precision is rather challenging. Therefore, we introduce a mechanism named correspondence propagation to aggregate each seed match into a set of numerous matches. With these sets of matches, multiple transformations between point clouds are computed. A quality function formulated from distance errors is used to identify the best transformation and fulfill a coarse alignment of the point clouds. Finally, we refine the alignment result with the trimmed iterative closest point algorithm. The proposed approach can be applied to register point clouds with significant or limited overlaps and small or large transformations. More encouragingly, it is rather efficient and very robust to noise. A comparison to traditional descriptor-based methods and other global algorithms demonstrates the fine performance of the proposed approach. We also show its promising application in large-scale reconstruction with the scans of two real scenes. In addition, the proposed approach can be used to register low-resolution point clouds captured by Kinect as well.

Martin Alain;Christine Guillemot;Dominique Thoreau;Philippe Guillotel; "Scalable Image Coding Based on Epitomes," vol.26(8), pp.3624-3635, Aug. 2017. In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome. An epitome can be seen as a factorized representation of an image. Focusing on spatial scalability, the enhancement layer of the proposed scheme contains only the epitome of the input image. The pixels of the enhancement layer not contained in the epitome are then restored using two approaches inspired from local learning-based super-resolution methods. In the first method, a locally linear embedding model is learned on base layer patches and then applied to the corresponding epitome patches to reconstruct the enhancement layer. The second approach learns linear mappings between pairs of co-located base layer and epitome patches. Experiments have shown that the significant improvement of the rate-distortion performances can be achieved compared with the Scalable extension of HEVC (SHVC).

Yueqi Duan;Jiwen Lu;Jianjiang Feng;Jie Zhou; "Learning Rotation-Invariant Local Binary Descriptor," vol.26(8), pp.3636-3651, Aug. 2017. In this paper, we propose a rotation-invariant local binary descriptor (RI-LBD) learning method for visual recognition. Compared with hand-crafted local binary descriptors, such as local binary pattern and its variants, which require strong prior knowledge, local binary feature learning methods are more efficient and data-adaptive. Unlike existing learning-based local binary descriptors, such as compact binary face descriptor and simultaneous local binary feature learning and encoding, which are susceptible to rotations, our RI-LBD first categorizes each local patch into a rotational binary pattern (RBP), and then jointly learns the orientation for each pattern and the projection matrix to obtain RI-LBDs. As all the rotation variants of a patch belong to the same RBP, they are rotated into the same orientation and projected into the same binary descriptor. Then, we construct a codebook by a clustering method on the learned binary codes, and obtain a histogram feature for each image as the final representation. In order to exploit higher order statistical information, we extend our RI-LBD to the triple rotation-invariant co-occurrence local binary descriptor (TRICo-LBD) learning method, which learns a triple co-occurrence binary code for each local patch. Extensive experimental results on four different visual recognition tasks, including image patch matching, texture classification, face recognition, and scene classification, show that our RI-LBD and TRICo-LBD outperform most existing local descriptors.

Xuelong Li;Bin Zhao;Xiaoqiang Lu; "A General Framework for Edited Video and Raw Video Summarization," vol.26(8), pp.3652-3664, Aug. 2017. In this paper, we build a general summarization framework for both of edited video and raw video summarization. Overall, our work can be divided into three folds. 1) Four models are designed to capture the properties of video summaries, i.e., containing important people and objects (importance), representative to the video content (representativeness), no similar key-shots (diversity), and smoothness of the storyline (storyness). Specifically, these models are applicable to both edited videos and raw videos. 2) A comprehensive score function is built with the weighted combination of the aforementioned four models. Note that the weights of the four models in the score function, denoted as property-weight, are learned in a supervised manner. Besides, the property-weights are learned for edited videos and raw videos, respectively. 3) The training set is constructed with both edited videos and raw videos in order to make up the lack of training data. Particularly, each training video is equipped with a pair of mixing-coefficients, which can reduce the structure mess in the training set caused by the rough mixture. We test our framework on three data sets, including edited videos, short raw videos, and long raw videos. Experimental results have verified the effectiveness of the proposed framework.

Qi Jia;Xin Fan;Zhongxuan Luo;Lianbo Song;Tie Qiu; "A Fast Ellipse Detector Using Projective Invariant Pruning," vol.26(8), pp.3665-3679, Aug. 2017. Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the intrinsic geometry of a planar curve, giving the value of −1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> matrix. Extensive experiments on three challenging data sets with 648 images demonstrate that our detector runs 20%–50% faster than the state-of-the-art algorithms with the comparable or higher precision.

Naushad Ansari;Anubha Gupta; "Image Reconstruction Using Matched Wavelet Estimated From Data Sensed Compressively Using Partial Canonical Identity Matrix," vol.26(8), pp.3680-3695, Aug. 2017. This paper proposes a joint framework wherein lifting-based, separable, image-matched wavelets are estimated from compressively sensed images and are used for the reconstruction of the same. Matched wavelet can be easily designed if full image is available. Also compared with the standard wavelets as sparsifying bases, matched wavelet may provide better reconstruction results in compressive sensing (CS) application. Since in CS application, we have compressively sensed images instead of full images, existing methods of designing matched wavelets cannot be used. Thus, we propose a joint framework that estimates matched wavelets from compressively sensed images and also reconstructs full images. This paper has three significant contributions. First, a lifting-based, image-matched separable wavelet is designed from compressively sensed images and is also used to reconstruct the same. Second, a simple sensing matrix is employed to sample data at sub-Nyquist rate such that sensing and reconstruction time is reduced considerably. Third, a new multi-level L-Pyramid wavelet decomposition strategy is provided for separable wavelet implementation on images that leads to improved reconstruction performance. Compared with the CS-based reconstruction using standard wavelets with Gaussian sensing matrix and with existing wavelet decomposition strategy, the proposed methodology provides faster and better image reconstruction in CS application.

Mikhail G. Mozerov;Joost van de Weijer; "Improved Recursive Geodesic Distance Computation for Edge Preserving Filter," vol.26(8), pp.3696-3706, Aug. 2017. All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application, we consider a geodesic distance-based filter for image denoising. Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than state-of-the-art approaches, while our algorithm is considerably faster with computational complexity O(8P).

Zhizhong Han;Zhenbao Liu;Chi-Man Vong;Yu-Shen Liu;Shuhui Bu;Junwei Han;C. L. Philip Chen; "BoSCC: Bag of Spatial Context Correlations for Spatially Enhanced 3D Shape Representation," vol.26(8), pp.3707-3720, Aug. 2017. Highly discriminative 3D shape representations can be formed by encoding the spatial relationship among virtual words into the Bag of Words (BoW) method. To achieve this challenging task, several unresolved issues in the encoding procedure must be overcome for 3D shapes, including: 1) arbitrary mesh resolution; 2) irregular vertex topology; 3) orientation ambiguity on the 3D surface; and 4) invariance to rigid and non-rigid shape transformations. In this paper, a novel spatially enhanced 3D shape representation called bag of spatial context correlations (BoSCCs) is proposed to address all these issues. Adopting a novel local perspective, BoSCC is able to describe a 3D shape by an occurrence frequency histogram of spatial context correlation patterns, which makes BoSCC become more compact and discriminative than previous global perspective-based methods. Specifically, the spatial context correlation is proposed to simultaneously encode the geometric and spatial information of a 3D local region by the correlation among spatial contexts of vertices in that region, which effectively resolves the aforementioned issues. The spatial context of each vertex is modeled by Markov chains in a multi-scale manner, which thoroughly captures the spatial relationship by the transition probabilities of intra-virtual words and the ones of inter-virtual words. The high discriminability and compactness of BoSCC are effective for classification and retrieval, especially in the scenarios of limited samples and partial shape retrieval. Experimental results show that BoSCC outperforms the state-of-the-art spatially enhanced BoW methods in three common applications: global shape retrieval, shape classification, and partial shape retrieval.

Chia-Liang Tsai;Shao-Yi Chien; "Feasible and Robust Optimization Framework for Auxiliary Information Refinement in Spatially-Varying Image Enhancement," vol.26(8), pp.3721-3733, Aug. 2017. In content-based image processing, the precise inference of auxiliary information dominates various image enhancement applications. Given the rough auxiliary information provided by users or inference algorithms, a common scenario is to refine it with respect to the image content. Quadratic Laplacian regularization is generally used as the refinement framework because of the availability of closed-form solutions. However, solving the resultant large linear system imposes a great burden on commodity computing hardware systems in the form of computational time and memory consumption, so efficient computing algorithms without losing precision are required, especially for large images. In this paper, we first analyze the geometric nature of the quadratic Laplacian regularization associated with the algebraic property of the corresponding linear system, which clarifies the essential issues causing ineffective solutions for conventional optimization algorithms. Correspondingly, we propose an optimization scheme that is capable of approaching the closed-form solution in an efficient manner using existing fast local filters, and we perform a spectral analysis to validate the robustness of this method in severe conditions. Finally, experimental results show that the proposed scheme is more feasible for large input images and is more robust to obtain the effective refinement than conventional algorithms.

Xueming Qian;Dan Lu;Yaxiong Wang;Li Zhu;Yuan Yan Tang;Meng Wang; "Image Re-Ranking Based on Topic Diversity," vol.26(8), pp.3734-3747, Aug. 2017. Social media sharing Websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then, the community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr data set and NUS-Wide data sets show the effectiveness of the proposed approach.

Yingjie Xia;Luming Zhang;Zhenguang Liu;Liqiang Nie;Xuelong Li; "Weakly Supervised Multimodal Kernel for Categorizing Aerial Photographs," vol.26(8), pp.3748-3758, Aug. 2017. Accurately distinguishing aerial photographs from different categories is a promising technique in computer vision. It can facilitate a series of applications, such as video surveillance and vehicle navigation. In this paper, a new image kernel is proposed for effectively recognizing aerial photographs. The key is to encode high-level semantic cues into local image patches in a weakly supervised way, and integrate multimodal visual features using a newly developed hashing algorithm. The flowchart can be elaborated as follows. Given an aerial photo, we first extract a number of graphlets to describe its topological structure. For each graphlet, we utilize color and texture to capture its appearance, and a weakly supervised algorithm to capture its semantics. Thereafter, aerial photo categorization can be naturally formulated as graphlet-to-graphlet matching. As the number of graphlets from each aerial photo is huge, to accelerate matching, we present a hashing algorithm to seamlessly fuze the multiple visual features into binary codes. Finally, an image kernel is calculated by fast matching the binary codes corresponding to each graphlet. And a multi-class SVM is learned for aerial photo categorization. We demonstrate the advantage of our proposed model by comparing it with state-of-the-art image descriptors. Moreover, an in-depth study of the descriptiveness of the hash-based graphlet is presented.

Sungryull Sohn;Hyunwoo Kim;Junmo Kim; "Uncorrelated Component Analysis-Based Hashing," vol.26(8), pp.3759-3774, Aug. 2017. The approximate nearest neighbor (ANN) search problem is important in applications such as information retrieval. Several hashing-based search methods that provide effective solutions to the ANN search problem have been proposed. However, most of these focus on similarity preservation and coding error minimization, and pay little attention to optimizing the precision–recall curve or receiver operating characteristic curve. In this paper, we propose a novel projection-based hashing method that attempts to maximize precision and recall. We first introduce an uncorrelated component analysis (UCA) transformation by examining precision and recall, and then propose a UCA-based hashing method. The proposed method is evaluated with a variety of data sets. The results show that UCA-based hashing outperforms state-of-the-art methods, and has computationally efficient training and encoding processes.

Miao Yu;Shuhan Shen;Zhanyi Hu; "Dynamic Graph Cuts in Parallel," vol.26(8), pp.3775-3788, Aug. 2017. This paper aims at bridging the two important trends in efficient graph cuts in the literature, the one is to decompose a graph into several smaller subgraphs to take the advantage of parallel computation, the other is to reuse the solution of the max-flow problem on a residual graph to boost the efficiency on another similar graph. Our proposed parallel dynamic graph cuts algorithm takes the advantages of both, and is extremely efficient for certain dynamically changing MRF models in computer vision. The performance of our proposed algorithm is validated on two typical dynamic graph cuts problems: the foreground-background segmentation in video, where similar graph cuts problems need to be solved in sequential and GrabCut, where graph cuts are used iteratively.

Heeseok Oh;Jongyoo Kim;Jinwoo Kim;Taewan Kim;Sanghoon Lee;Alan Conrad Bovik; "Enhancement of Visual Comfort and Sense of Presence on Stereoscopic 3D Images," vol.26(8), pp.3789-3801, Aug. 2017. Conventional stereoscopic 3D (S3D) displays do not provide accommodation depth cues of the 3D image or video contents being viewed. The sense of content depths is thus limited to cues supplied by motion parallax (for 3D video), stereoscopic vergence cues created by presenting left and right views to the respective eyes, and other contextual and perspective depth cues. The absence of accommodation cues can induce two kinds of accommodation vergence mismatches (AVM) at the fixation and peripheral points, which can result in severe visual discomfort. With the aim of alleviating discomfort arising from AVM, we propose a new visual comfort enhancement approach for processing S3D visual signals to deliver a more comfortable 3D viewing experience at the display. This is accomplished via an optimization process whereby a predictive indicator of visual discomfort is minimized, while still aiming to maintain the viewer’s sense of 3D presence by performing a suitable parallax shift, and by directed blurring of the signal. Our processing framework is defined on 3D visual coordinates that reflect the nonuniform resolution of retinal sensors and that uses a measure of 3D saliency strength. An appropriate level of blur that corresponds to the degree of parallax shift is found, making it possible to produce synthetic accommodation cues implemented using a perceptively relevant filter. By this method, AVM, the primary contributor to the discomfort felt when viewing S3D images, is reduced. We show via a series of subjective experiments that the proposed approach improves visual comfort while preserving the sense of 3D presence.

Jing Cui;Shanshe Wang;Shiqi Wang;Xinfeng Zhang;Siwei Ma;Wen Gao; "Hybrid Laplace Distribution-Based Low Complexity Rate-Distortion Optimized Quantization," vol.26(8), pp.3802-3816, Aug. 2017. Rate distortion optimized quantization (RDOQ) is an efficient encoder optimization method that plays an important role in improving the rate-distortion (RD) performance of the high-efficiency video coding (HEVC) codecs. However, the superior performance of RDOQ is achieved at the expense of high computational complexity cost in two stages RD minimization, including the determination of optimal quantized level among available candidates for each transformed coefficient and the determination of best quantized coefficients for transform units with the minimum total cost, to softly optimize the quantized coefficients. To reduce the computational cost of the RDOQ algorithm in HEVC, we propose a low-complexity RDOQ scheme by modeling the statistics of the transform coefficients with hybrid Laplace distribution. In this manner, specifically designed block level rate and distortion models are established based on the coefficient distribution. Therefore, the optimal quantization levels can be directly determined by optimizing the RD performance of the whole block, while the complicated RD cost calculations can be eventually avoided. Extensive experimental results show that with about 0.3%−0.4% RD performance degradation, the proposed low-complexity RDOQ algorithm is able to reduce around 70% quantization time with up to 17% total encoding time reduction compared with the original RDOQ implementation in HEVC on average.

Han-Ul Kim;Chang-Su Kim; "Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor," vol.26(8), pp.3817-3830, Aug. 2017. In this paper, we propose a simple yet effective object descriptor and a novel tracking algorithm to track a target object accurately. For the object description, we divide the bounding box of a target object into multiple patches and describe them with color and gradient histograms. Then, we determine the foreground weight of each patch to alleviate the impacts of background information in the bounding box. To this end, we perform random walk with restart (RWR) simulation. We then concatenate the weighted patch descriptors to yield the spatially ordered and weighted patch (SOWP) descriptor. For the object tracking, we incorporate the proposed SOWP descriptor into a novel tracking algorithm, which has three components: locator, checker, and scaler (LCS). The locator and the scaler estimate the center location and the size of a target, respectively. The checker determines whether it is safe to adjust the target scale in a current frame. These three components cooperate with one another to achieve robust tracking. Experimental results demonstrate that the proposed LCS tracker achieves excellent performance on recent benchmarks.

Lazhar Khelifi;Max Mignotte; "A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem," vol.26(8), pp.3831-3845, Aug. 2017. Image segmentation fusion is defined as the set of methods which aim at merging several image segmentations, in a manner that takes full advantage of the complementarity of each one. Previous relevant researches in this field have been impeded by the difficulty in identifying an appropriate single segmentation fusion criterion, providing the best possible, i.e., the more informative, result of fusion. In this paper, we propose a new model of image segmentation fusion based on multi-objective optimization which can mitigate this problem, to obtain a final improved result of segmentation. Our fusion framework incorporates the dominance concept in order to efficiently combine and optimize two complementary segmentation criteria, namely, the global consistency error and the F-measure (precision-recall) criterion. To this end, we present a hierarchical and efficient way to optimize the multi-objective consensus energy function related to this fusion model, which exploits a simple and deterministic iterative relaxation strategy combining the different image segments. This step is followed by a decision making task based on the so-called “technique for order performance by similarity to ideal solution”. Results obtained on two publicly available databases with manual ground truth segmentations clearly show that our multi-objective energy-based model gives better results than the classical mono-objective one.

Zhouzhou He;Xi Li;Zhongfei Zhang;Fei Wu;Xin Geng;Yaqing Zhang;Ming-Hsuan Yang;Yueting Zhuang; "Data-Dependent Label Distribution Learning for Age Estimation," vol.26(8), pp.3846-3858, Aug. 2017. As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with respect to several ordinal age labels, which have intrinsically cross-age correlations across adjacent age dimensions. As a result, such correlations usually lead to the age label ambiguities of the face samples. Namely, each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. Motivated by this observation, we propose a totally data-driven label distribution learning approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the basis of the local context structures of face samples. Without any prior assumptions on the forms of label distribution learning, our approach is able to flexibly model the sample-specific context aware label distribution properties by solving a multi-task problem, which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental results demonstrate the effectiveness of our approach.

Xiudong Wang;Yuantao Gu; "Cross-Label Suppression: A Discriminative and Fast Dictionary Learning With Group Regularization," vol.26(8), pp.3859-3873, Aug. 2017. This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose cross-label suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we donot resort to frequently-used <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-norm or <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets, including face recognition, object categorization, scene classification, texture recognition, and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.

Yu Li;Robby T. Tan;Xiaojie Guo;Jiangbo Lu;Michael S. Brown; "Single Image Rain Streak Decomposition Using Layer Priors," vol.26(8), pp.3874-3885, Aug. 2017. Rain streaks impair visibility of an image and introduce undesirable interference that can severely affect the performance of computer vision and image analysis systems. Rain streak removal algorithms try to recover a rain streak free background scene. In this paper, we address the problem of rain streak removal from a single image by formulating it as a layer decomposition problem, with a rain streak layer superimposed on a background layer containing the true scene content. Existing decomposition methods that address this problem employ either sparse dictionary learning methods or impose a low rank structure on the appearance of the rain streaks. While these methods can improve the overall visibility, their performance can often be unsatisfactory, for they tend to either over-smooth the background images or generate -images that still contain noticeable rain streaks. To address the problems, we propose a method that imposes priors for both the background and rain streak layers. These priors are based on Gaussian mixture models learned on small patches that can accommodate a variety of background appearances as well as the appearance of the rain streaks. Moreover, we introduce a structure residue recovery step to further separate the background residues and improve the decomposition quality. Quantitative evaluation shows our method outperforms existing methods by a large margin. We overview our method and demonstrate its effectiveness over prior work on a number of examples.

Ricardo L. de Queiroz;Philip A. Chou; "Motion-Compensated Compression of Dynamic Voxelized Point Clouds," vol.26(8), pp.3886-3895, Aug. 2017. Dynamic point clouds are a potential new frontier in visual communication systems. A few articles have addressed the compression of point clouds, but very few references exist on exploring temporal redundancies. This paper presents a novel motion-compensated approach to encoding dynamic voxelized point clouds at low bit rates. A simple coder breaks the voxelized point cloud at each frame into blocks of voxels. Each block is either encoded in intra-frame mode or is replaced by a motion-compensated version of a block in the previous frame. The decision is optimized in a rate-distortion sense. In this way, both the geometry and the color are encoded with distortion, allowing for reduced bit-rates. In-loop filtering is employed to minimize compression artifacts caused by distortion in the geometry information. Simulations reveal that this simple motion-compensated coder can efficiently extend the compression range of dynamic voxelized point clouds to rates below what intra-frame coding alone can accommodate, trading rate for geometry accuracy.

Zhen-Qun Yang;Xiao-Yong Wei;Zhang Yi;Gerald Friedland; "Contextual Noise Reduction for Domain Adaptive Near-Duplicate Retrieval on Merchandize Images," vol.26(8), pp.3896-3910, Aug. 2017. In this paper, we have proposed a novel method, which utilizes the contextual relationship among visual words for reducing the quantization errors in near-duplicate image retrieval (NDR). Instead of following the track of conventional NDR techniques, which usually search new solutions by borrowing ideas from the text domain, we propose to model the problem back to image domain, which results in a more natural way of solution search. The idea of the proposed method is to construct a context graph that encapsulates the contextual relationship within an image and treat the graph as a pseudo-image, so that classical image filters can be adopted to reduce the mis-mapped visual words which are contextually inconsistent with others. With these contextual noises reduced, the method provides purified inputs to the subsequent processes in NDR, and improves the overall accuracy. More importantly, the purification further increases the sparsity of the image feature vectors, which thus speeds up the conventional methods by 1662% times and makes NDR practical to online applications on merchandize images where the requirement of response time is critical. The way of considering contextual noise reduction in image domain also makes the problem open to all sophisticated filters. Our study shows the classic anisotropic diffusion filter can be employed to address the cross-domain issue, resulting in the superiority of the method to conventional ones in both effectiveness and efficiency.

Xiaojun Chang;Zhigang Ma;Ming Lin;Yi Yang;Alexander G. Hauptmann; "Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection," vol.26(8), pp.3911-3920, Aug. 2017. The Kinect sensing devices have been widely used in current Human-Computer Interaction entertainment. A fundamental issue involved is to detect users’ motions accurately and quickly. In this paper, we tackle it by proposing a linear algorithm, which is augmented by feature interaction. The linear property guarantees its speed whereas feature interaction captures the higher order effect from the data to enhance its accuracy. The Schatten-p norm is leveraged to integrate the main linear effect and the higher order nonlinear effect by mining the correlation between them. The resulted classification model is a desirable combination of speed and accuracy. We propose a novel solution to solve our objective function. Experiments are performed on three public Kinect-based entertainment data sets related to fitness and gaming. The results show that our method has its advantage for motion detection in a real-time Kinect entertaining environment.

Chia-Sung Chang;Jau-Ji Shen; "Features Classification Forest: A Novel Development that is Adaptable to Robust Blind Watermarking Techniques," vol.26(8), pp.3921-3935, Aug. 2017. A novel watermarking scheme is proposed that could substantially improve current watermarking techniques. This scheme exploits the features of micro images of watermarks to build association rules and embeds the rules into a host image instead of the bit stream of the watermark, which is commonly used in digital watermarking. Next, similar micro images with the same rules are collected or even created from the host image to simulate an extracted watermark. This method, called the features classification forest, can achieve blind extraction and is adaptable to any watermarking scheme using a quantization-based mechanism. Furthermore, a larger size watermark can be accepted without an adverse effect on the imperceptibility of the host image. The experiments demonstrate the successful simulation of watermarks and the application to five different watermarking schemes. One of them is slightly adjusted from a reference to especially resist JPEG compression, and the others show native advantages to resist different image processing attacks.

Yinglong Wang;Shuaicheng Liu;Chen Chen;Bing Zeng; "A Hierarchical Approach for Rain or Snow Removing in a Single Color Image," vol.26(8), pp.3936-3950, Aug. 2017. In this paper, we propose an efficient algorithm to remove rain or snow from a single color image. Our algorithm takes advantage of two popular techniques employed in image processing, namely, image decomposition and dictionary learning. At first, a combination of rain/snow detection and a guided filter is used to decompose the input image into a complementary pair: 1) the low-frequency part that is free of rain or snow almost completely and 2) the high-frequency part that contains not only the rain/snow component but also some or even many details of the image. Then, we focus on the extraction of image’s details from the high-frequency part. To this end, we design a 3-layer hierarchical scheme. In the first layer, an overcomplete dictionary is trained and three classifications are carried out to classify the high-frequency part into rain/snow and non-rain/snow components in which some common characteristics of rain/snow have been utilized. In the second layer, another combination of rain/snow detection and guided filtering is performed on the rain/snow component obtained in the first layer. In the third layer, the sensitivity of variance across color channels is computed to enhance the visual quality of rain/snow-removed image. The effectiveness of our algorithm is verified through both subjective (the visual quality) and objective (through rendering rain/snow on some ground-truth images) approaches, which shows a superiority over several state-of-the-art works.

Kede Ma;Wentao Liu;Tongliang Liu;Zhou Wang;Dacheng Tao; "dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs," vol.26(8), pp.3951-3964, Aug. 2017. Objective assessment of image quality is fundamentally important in many image processing tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models, which predict the quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training. Such data are typically collected via subjective testing, which is cumbersome, slow, and expensive. Here, we first show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIPs) can be obtained automatically at low cost by exploiting large-scale databases with diverse image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no subjective opinions are used for training) model using RankNet, a pairwise learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index. Extensive experiments on four benchmark IQA databases demonstrate that dipIQ outperforms the state-of-the-art OU-BIQA models. The robustness of dipIQ is also significantly improved as confirmed by the group MAximum Differentiation competition method. Furthermore, we extend the proposed framework by learning models with ListNet (a listwise L2R algorithm) on quality-discriminable image lists (DIL). The resulting DIL inferred quality index achieves an additional performance gain.

Sezer Karaoglu;Ran Tao;Jan C. van Gemert;Theo Gevers; "Con-Text: Text Detection for Fine-Grained Object Classification," vol.26(8), pp.3965-3980, Aug. 2017. This paper focuses on fine-grained object classification using recognized scene text in natural images. While the state-of-the-art relies on visual cues only, this paper is the first work which proposes to combine textual and visual cues. Another novelty is the textual cue extraction. Unlike the state-of-the-art text detection methods, we focus more on the background instead of text regions. Once text regions are detected, they are further processed by two methods to perform text recognition, i.e., ABBYY commercial OCR engine and a state-of-the-art character recognition algorithm. Then, to perform textual cue encoding, bi- and trigrams are formed between the recognized characters by considering the proposed spatial pairwise constraints. Finally, extracted visual and textual cues are combined for fine-grained classification. The proposed method is validated on four publicly available data sets: ICDAR03, ICDAR13, Con-Text, and Flickr-logo. We improve the state-of-the-art end-to-end character recognition by a large margin of 15% on ICDAR03. We show that textual cues are useful in addition to visual cues for fine-grained classification. We show that textual cues are also useful for logo retrieval. Adding textual cues outperforms visual- and textual-only in fine-grained classification (70.7% to 60.3%) and logo retrieval (57.4% to 54.8%).

Huanjing Yue;Jingyu Yang;Xiaoyan Sun;Feng Wu;Chunping Hou; "Contrast Enhancement Based on Intrinsic Image Decomposition," vol.26(8), pp.3981-3994, Aug. 2017. In this paper, we propose to introduce intrinsic image decomposition priors into decomposition models for contrast enhancement. Since image decomposition is a highly ill-posed problem, we introduce constraints on both reflectance and illumination layers to yield a highly reliable solution. We regularize the reflectance layer to be piecewise constant by introducing a weighted <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> norm constraint on neighboring pixels according to the color similarity, so that the decomposed reflectance would not be affected much by the illumination information. The illumination layer is regularized by a piecewise smoothness constraint. The proposed model is effectively solved by the Split Bregman algorithm. Then, by adjusting the illumination layer, we obtain the enhancement result. To avoid potential color artifacts introduced by illumination adjusting and reduce computing complexity, the proposed decomposition model is performed on the value channel in HSV space. Experiment results demonstrate that the proposed method performs well for a wide variety of images, and achieves better or comparable subjective and objective quality compared with the state-of-the-art methods.

Nilotpal Das;Devraj Mandal;Soma Biswas; "Simultaneous Semi-Coupled Dictionary Learning for Matching in Canonical Space," vol.26(8), pp.3995-4004, Aug. 2017. Cross-modal recognition and matching with privileged information are important challenging problems in the field of computer vision. The cross-modal scenario deals with matching across different modalities and needs to take care of the large variations present across and within each modality. The privileged information scenario deals with the situation that all the information available during training may not be available during the testing stage, and hence, algorithms need to leverage the extra information from the training stage itself. We show that for multi-modal data, either one of the above situations may arise if one modality is absent during testing. Here, we propose a novel framework, which can handle both these scenarios seamlessly with applications to matching multi-modal data. The proposed approach jointly uses data from the two modalities to build a canonical representation, which encompasses information from both the modalities. We explore four different types of canonical representations for different types of data. The algorithm computes dictionaries and canonical representation for data from both the modalities, such that the transformed sparse coefficients of both the modalities are equal to that of the canonical representation. The sparse coefficients are finally matched using Mahalanobis metric. Extensive experiments on different data sets, involving RGBD, text-image, and audio-image data, show the effectiveness of the proposed framework.

IEEE Transactions on Medical Imaging - new TOC (2017 June 19) [Website]

* "Table of contents," vol.36(6), pp.C1-C4, June 2017.* Presents the table of contents for this issue of the publication.

* "IEEE Transactions on Medical Imaging publication information," vol.36(6), pp.C2-C2, June 2017.* Presents a listing of the editorial board, board of governors, current staff, committee members, and society editors for this issue of the publication.

Andres Saucedo;Stamatios Lefkimmiatis;Novena Rangwala;Kyunghyun Sung; "Improved Computational Efficiency of Locally Low Rank MRI Reconstruction Using Iterative Random Patch Adjustments," vol.36(6), pp.1209-1220, June 2017. This paper presents and analyzes an alternative formulation of the locally low-rank (LLR) regularization framework for magnetic resonance image (MRI) reconstruction. Generally, LLR-based MRI reconstruction techniques operate by dividing the underlying image into a collection of matrices formed from image patches. Each of these matrices is assumed to have low rank due to the inherent correlations among the data, whether along the coil, temporal, or multi-contrast dimensions. The LLR regularization has been successful for various MRI applications, such as parallel imaging and accelerated quantitative parameter mapping. However, a major limitation of most conventional implementations of the LLR regularization is the use of multiple sets of overlapping patches. Although the use of overlapping patches leads to effective shift-invariance, it also results in high-computational load, which limits the practical utility of the LLR regularization for MRI. To circumvent this problem, alternative LLR-based algorithms instead shift a single set of non-overlapping patches at each iteration, thereby achieving shift-invariance and avoiding block artifacts. A novel contribution of this paper is to provide a mathematical framework and justification of LLR regularization with iterative random patch adjustments (LLR-IRPA). This method is compared with a state-of-the-art LLR regularization algorithm based on overlapping patches, and it is shown experimentally that results are similar but with the advantage of much reduced computational load. We also present theoretical results demonstrating the effective shift invariance of the LLR-IRPA approach, and we show reconstruction examples and comparisons in both retrospectively and prospectively undersampled MRI acquisitions, and in T1 parameter mapping.

Amir H. Abdi;Christina Luong;Teresa Tsang;Gregory Allan;Saman Nouranian;John Jue;Dale Hawley;Sarah Fleming;Ken Gin;Jody Swift;Robert Rohling;Purang Abolmaesumi; "Automatic Quality Assessment of Echocardiograms Using Convolutional Neural Networks: Feasibility on the Apical Four-Chamber View," vol.36(6), pp.1221-1230, June 2017. Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert's manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.

Jorge Bernal;Nima Tajkbaksh;Francisco Javier Sánchez;Bogdan J. Matuszewski;Hao Chen;Lequan Yu;Quentin Angermann;Olivier Romain;Bjørn Rustad;Ilangko Balasingham;Konstantin Pogorelov;Sungbin Choi;Quentin Debard;Lena Maier-Hein;Stefanie Speidel;Danail Stoyanov;Patrick Brandao;Henry Córdova;Cristina Sánchez-Montes;Suryakanth R. Gurudu;Gloria Fernández-Esparrach;Xavier Dray;Jianming Liang;Aymeric Histace; "Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge," vol.36(6), pp.1231-1249, June 2017. Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.

Guang-Quan Zhou;Wei-Wei Jiang;Ka-Lee Lai;Yong-Ping Zheng; "Automatic Measurement of Spine Curvature on 3-D Ultrasound Volume Projection Image With Phase Features," vol.36(6), pp.1250-1262, June 2017. This paper presents an automated measurement of spine curvature by using prior knowledge on vertebral anatomical structures in ultrasound volume projection imaging (VPI). This method can be used in scoliosis assessment with free-hand 3-D ultrasound imaging. It is based on the extraction of bony features from VPI images using a newly proposed two-fold thresholding strategy, with information of the symmetric and asymmetric measures obtained from phase congruency. The spinous column profile is detected from the segmented bony regions, and it is further used to extract a curve representing spine profile. The spine curvature is then automatically calculated according to the inflection points along the curve. The algorithm was evaluated on volunteers with the different severity of scoliosis. The results obtained using the newly developed method had a good linear correlation with those by the manual method (r ≥ 0.90, p <; 0.001) and X-ray Cobb's method (r = 0.83, p <; 0.001). The bigger variations observed in the manual measurement also implied that the automatic method is more reliable. The proposed method can be a promising approach for facilitating the applications of 3-D ultrasound imaging in the diagnosis, treatment, and screening of scoliosis.

Ivan S. Klyuzhin;Vesna Sossi; "PET Image Reconstruction and Deformable Motion Correction Using Unorganized Point Clouds," vol.36(6), pp.1263-1275, June 2017. Quantitative positron emission tomography imaging often requires correcting the image data for deformable motion. With cyclic motion, this is traditionally achieved by separating the coincidence data into a relatively small number of gates, and incorporating the inter-gate image transformation matrices into the reconstruction algorithm. In the presence of non-cyclic deformable motion, this approach may be impractical due to a large number of required gates. In this paper, we propose an alternative approach to iterative image reconstruction with correction for deformable motion, wherein unorganized point clouds are used to model the imaged objects in the image space, and motion is corrected for explicitly by introducing a time-dependence into the point coordinates. The image function is represented using constant basis functions with finite support determined by the boundaries of the Voronoi cells in the point cloud. We validate the quantitative accuracy and stability of the proposed approach by reconstructing noise-free and noisy projection data from digital and physical phantoms. The point-cloud-based maximum likelihood expectation maximization (MLEM) and one-pass list-mode ordered-subset expectation maximization (OSEM) algorithms are validated. The results demonstrate that images reconstructed using the proposed method are quantitatively stable, with noise and convergence properties comparable to image reconstruction based on the use of rectangular and radially-symmetric basis functions.

Jelena Novosel;Koenraad A. Vermeer;Jan H. de Jong;Ziyuan Wang;Lucas J. van Vliet; "Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas," vol.36(6), pp.1276-1286, June 2017. Accurate quantification of retinal structures in 3-D optical coherence tomography data of eyes with pathologies provides clinically relevant information. We present an approach to jointly segment retinal layers and lesions in eyes with topology-disrupting retinal diseases by a loosely coupled level set framework. In the new approach, lesions are modeled as an additional space-variant layer delineated by auxiliary interfaces. Furthermore, the segmentation of interfaces is steered by local differences in the signal between adjacent retinal layers, thereby allowing the approach to handle local intensity variations. The accuracy of the proposed method of both layer and lesion segmentation has been evaluated on eyes affected by central serous retinopathy and age-related macular degeneration. In addition, layer segmentation of the proposed approach was evaluated on eyes without topology-disrupting retinal diseases. Good agreement between the segmentation performed manually by a medical doctor and results obtained from the automatic segmentation was found for all data types. The mean unsigned error for all interfaces varied between 2.3 and 11.9 μm (0.6-3.1 pixels). Furthermore, lesion segmentation showed a Dice coefficient of 0.68 for drusen and 0.89 for fluid pockets. Overall, the method provides a flexible and accurate solution to jointly segment lesions and retinal layers.

Mathias Schwarz;Dominik Soliman;Murad Omar;Andreas Buehler;Saak V. Ovsepian;Juan Aguirre;Vasilis Ntziachristos; "Optoacoustic Dermoscopy of the Human Skin: Tuning Excitation Energy for Optimal Detection Bandwidth With Fast and Deep Imaging in vivo," vol.36(6), pp.1287-1296, June 2017. Optoacoustic (photoacoustic) dermoscopy offers two principal advantages over conventional optical imaging applied in dermatology. First, it yields high-resolution cross-sectional images of the skin at depths not accessible to other non-invasive optical imaging methods. Second, by resolving absorption spectra at multiple wavelengths, it enables label-free 3D visualization of morphological and functional features. However, the relation of pulse energy to generated bandwidth and imaging depth remains poorly defined. In this paper, we apply computer models to investigate the optoacoustic frequency response generated by simulated skin. We relate our simulation results to experimental measurements of the detection bandwidth as a function of optical excitation energy in phantoms and human skin. Using raster-scan optoacoustic mesoscopy, we further compare the performance of two broadband ultrasonic detectors (a bandwidth of 20-180 and 10-90MHz) in acquiring optoacoustic readouts. Based on the findings of this paper, we propose energy ranges required for skin imaging with considerations of laser safety standards.

Evan H. Phillips;Paolo Di Achille;Matthew R. Bersi;Jay D. Humphrey;Craig J. Goergen; "Multi-Modality Imaging Enables Detailed Hemodynamic Simulations in Dissecting Aneurysms in Mice," vol.36(6), pp.1297-1305, June 2017. A multi-modality imaging-based modeling approach was used to study complex unsteady hemodynamics and lesion growth in a dissecting abdominal aortic aneurysm model. We combined in vivo ultrasound (geometry and flow) and in vitro optical coherence tomography(OCT) (geometry) to obtain the high resolution needed to construct detailed hemodynamic simulations over large portions of the murine vasculature, which include fine geometric complexities. We illustrate this approach for a spectrum of dissecting abdominal aortic aneurysms induced in male apolipoprotein E-null mice by high-dose angiotensin II infusion. In vivo morphological and hemodynamic data provide information on volumetric lesion growth and changes in blood flow dynamics, respectively, occurring from the day of initial aortic expansion. We validated the associated computational models by comparing results on time-varying outlet flows and vortical structures within the lesions. Three out of four lesions exhibited abrupt formation of thrombus, though different in size. We determined that a lesion without thrombus formed with a thickened vessel wall, which was resolvable by OCT and histology. We attribute differences in final sizes and compositions of these lesions to the different computed flow and vortical structures we obtained in our mouse-specific fluid dynamic models. Differences in morphology and hemodynamics play crucial roles in determining the evolution of dissecting abdominal aortic aneurysms. Coupled high resolution in vivo and in vitro imaging approaches provide much-improved geometric models for hemodynamic simulations. Our imaging-based computational findings suggest a link between perturbations in hemodynamic metrics and aneurysmal disease heterogeneity.

Jelena Novosel;Suzanne Yzer;Koenraad A. Vermeer;Lucas J. van Vliet; "Segmentation of Locally Varying Numbers of Outer Retinal Layers by a Model Selection Approach," vol.36(6), pp.1306-1315, June 2017. Extraction of image-based biomarkers, such as the presence, visibility, or thickness of a certain layer, from 3-D optical coherence tomography data provides relevant clinical information. We present a method to simultaneously determine the number of visible layers in the outer retina and segment them. The method is based on a model selection approach with special attention given to the balance between the quality of a fit and model complexity. This will ensure that a more complex model is selected only if this is sufficiently supported by the data. The performance of the method was evaluated on healthy and retinitis pigmentosa (RP) affected eyes. In addition, the reproducibility of automatic method and manual annotations was evaluated on healthy eyes. Good agreement between the segmentation performed manually by a medical doctor and results obtained from the automatic segmentation was found. The mean unsigned deviation for all outer retinal layers in healthy and RP affected eyes varied between 2.6 and 4.9 μm. The reproducibility of the automatic method was similar to the reproducibility of the manual segmentation. Overall, the method provides a flexible and accurate solution for determining the visibility and location of outer retinal layers and could be used as an aid for the disease diagnosis and monitoring.

Joanne Bates;Irvin Teh;Darryl McClymont;Peter Kohl;Jürgen E. Schneider;Vicente Grau; "Monte Carlo Simulations of Diffusion Weighted MRI in Myocardium: Validation and Sensitivity Analysis," vol.36(6), pp.1316-1325, June 2017. A model of cardiac microstructure and diffusion MRI is presented, and compared with experimental data from ex vivo rat hearts. The model includes a simplified representation of individual cells, with physiologically correct cell size and orientation, as well as intra- to extracellular volume ratio. Diffusion MRI is simulated using a Monte Carlo model and realistic MRI sequences. The results show good correspondence between the simulated and experimental MRI signals. Similar patterns are observed in the eigenvalues of the diffusion tensor, the mean diffusivity (MD), and the fractional anisotropy (FA). A sensitivity analysis shows that the diffusivity is the dominant influence on all three eigenvalues of the diffusion tensor, the MD, and the FA. The area and aspect ratio of the cell cross-section affect the secondary and tertiary eigenvalues, and hence the FA. Within biological norms, the cell length, volume fraction of cells, and rate of change of helix angle play a relatively small role in influencing tissue diffusion. Results suggest that the model could be used to improve understanding of the relationship between cardiac microstructure and diffusion MRI measurements, as well as in testing and refinement of cardiac diffusion MRI protocols.

Chenxi Hu;Stanley Reeves;Dana C. Peters;Donald Twieg; "An Efficient Reconstruction Algorithm Based on the Alternating Direction Method of Multipliers for Joint Estimation of ${R}_{{2}}^{*}$ and Off-Resonance in fMRI," vol.36(6), pp.1326-1336, June 2017. R*2 mapping is a useful tool in blood-oxygen-level dependent fMRI due to its quantitative-nature. However, like T*2-weighted imaging, standard R*2 mapping based on multi-echo EPI suffers from geometric distortion, due to strong off-resonance near the air-tissue interface. Joint mapping of R*2 and off-resonance can correct the geometric distortion and is less susceptible to motion artifacts. Single-shot joint mapping of R*2 and off-resonance is possible with a rosette trajectory due to its frequent sampling of the k-space center. However, the corresponding reconstruction is nonlinear, ill-conditioned, large-scale, and computationally inefficient with current algorithms. In this paper, we propose a novel algorithm for joint mapping of R*2 and off-resonance, using rosette k-space trajectories. The new algorithm, based on the alternating direction method of multipliers, improves the reconstruction efficiency by simplifying the original complicated cost function into a composition of simpler optimization steps. Compared with a recently developed trust region algorithm, the new algorithm achieves the same accuracy and an acceleration of threefold to sixfold in reconstruction time. Based on the new algorithm, we present simulation and in vivo data from single-shot, double-shot, and quadruple-shot rosettes and demonstrate the improved image quality and reduction of distortions in the reconstructed R*2 map.

Hongbo Guo;Xiaowei He;Muhan Liu;Zeyu Zhang;Zhenhua Hu;Jie Tian; "Weight Multispectral Reconstruction Strategy for Enhanced Reconstruction Accuracy and Stability With Cerenkov Luminescence Tomography," vol.36(6), pp.1337-1346, June 2017. Cerenkov luminescence tomography (CLT) provides a novel technique for 3-D noninvasive detection of radiopharmaceuticals in living subjects. However, because of the severe scattering of Cerenkov light, the reconstruction accuracy and stability of CLT is still unsatisfied. In this paper, a modified weight multispectral CLT (wmCLT) reconstruction strategy was developed which split the Cerenkov radiation spectrum into several sub-spectral bands and weighted the sub-spectral results to obtain the final result. To better evaluate the property of the wmCLT reconstruction strategy in terms of accuracy, stability and practicability, several numerical simulation experiments and in vivo experiments were conducted and the results obtained were compared with the traditional multispectral CLT (mCLT) and hybrid-spectral CLT (hCLT) reconstruction strategies. The numerical simulation results indicated that wmCLT strategy significantly improved the accuracy of Cerenkov source localization and intensity quantitation and exhibited good stability in suppressing noise in numerical simulation experiments. And the comparison of the results achieved from different in vivo experiments further indicated significant improvement of the wmCLT strategy in terms of the shape recovery of the bladder and the spatial resolution of imaging xenograft tumors. Overall the strategy reported here will facilitate the development of nuclear and optical molecular tomography in theoretical study.

M. Omidyeganeh;Y. Xiao;M. O. Ahmad;H. Rivaz; "Estimation of Strain Elastography from Ultrasound Radio-Frequency Data by Utilizing Analytic Gradient of the Similarity Metric," vol.36(6), pp.1347-1358, June 2017. Most strain imaging techniques follow a pipeline strategy: in the first step, tissue displacement is estimated from radio-frequency (RF) frames, and in the second step, a spatial derivative operation is applied. There are two main issues that arise from this framework. First, the gradient operation amplifies noise, and therefore, smoothing techniques have to be adopted. Second, strain estimation does not exploit the original RF data. It rather relies solely on the noisy displacement field. In this paper, a novel technique is proposed that utilizes both the displacement field and the RF frames to accurately obtain the strain estimates. The normalized cross correlation (NCC) metric between two corresponding windows around the samples of the pre- and post-compressed images is employed to generate a dissimilarity measurement. The derivative of NCC with respect to the strain is analytically derived using the chain rule. This allows an efficient minimization of the dissimilarity metric with respect to the strain using the gradient descent optimization technique. The effectiveness of the proposed method is investigated through simulation data, phantom experiments, and in vivo patient data. The experimental results show that exploiting the information in RF data significantly improves the strain estimates.

Alexey A. Novikov;David Major;Maria Wimmer;Gert Sluiter;Katja Bühler; "Automated Anatomy-Based Tracking of Systemic Arteries in Arbitrary Field-of-View CTA Scans," vol.36(6), pp.1359-1371, June 2017. We propose an automated pipeline for vessel centerline extraction in 3-D computed tomography angiography (CTA) scans with arbitrary fields of view. The principal steps of the pipeline are body part detection, candidate seed selection, segment tracking, which includes centerline extraction, and vessel tree growing. The final tree-growing step can be instantiated in either a semi- or fully automated fashion. The fully automated initialization is carried out using a vessel position regression algorithm. Both semi-and fully automated methods were evaluated on 30 CTA scans comprising neck, abdominal, and leg arteries in multiple fields of view. High detection rates and centerline accuracy values for 38 distinct vessels demonstrate the effectiveness of our approach.

Ping Gong;Pengfei Song;Shigao Chen; "Ultrafast Synthetic Transmit Aperture Imaging Using Hadamard-Encoded Virtual Sources With Overlapping Sub-Apertures," vol.36(6), pp.1372-1381, June 2017. The development of ultrafast ultrasound imaging offers great opportunities to improve imaging technologies, such as shear wave elastography and ultrafast Doppler imaging. In ultrafast imaging, there are tradeoffs among image signal-to-noise ratio (SNR), resolution, and post-compounded frame rate. Various approaches have been proposed to solve this tradeoff, such as multiplane wave imaging or the attempts of implementing synthetic transmit aperture imaging. In this paper, we propose an ultrafast synthetic transmit aperture (USTA) imaging technique using Hadamard-encoded virtual sources with overlapping sub-apertures to enhance both image SNR and resolution without sacrificing frame rate. This method includes three steps: 1) create virtual sources using sub-apertures; 2) encode virtual sources using Hadamard matrix; and 3) add short time intervals (a few microseconds) between transmissions of different virtual sources to allow overlapping sub-apertures. The USTA was tested experimentally with a point target, a B-mode phantom, and in vivo human kidney micro-vessel imaging. Compared with standard coherent diverging wave compounding with the same frame rate, improvements on image SNR, lateral resolution (+33%, with B-mode phantom imaging), and contrast ratio (+3.8 dB, with in vivo human kidney micro-vessel imaging) have been achieved. The f-number of virtual sources, the number of virtual sources used, and the number of elements used in each sub-aperture can be flexibly adjusted to enhance resolution and SNR. This allows very flexible optimization of USTA for different applications.

* "NIH-IEEE 2017 Special Topics Conference on Healthcare Innovations and Point of Care Technologies," vol.36(6), pp.1382-1382, June 2017.* Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

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* "39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Keynote Speakers," vol.36(6), pp.1384-1384, June 2017.* Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

* "IEEE Transactions on Medical Imaging information for authors," vol.36(6), pp.C3-C3, June 2017.* These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

IET Image Processing - new TOC (2017 June 19) [Website]

Zhiyao Yang;Qinglong Shao;Shuxu Guo; "Fast coding algorithm for HEVC based on video contents," vol.11(6), pp.343-351, 6 2017. High efficiency video coding (HEVC) achieves higher coding efficiency than previous standards but introduces a large computational complexity. Because HEVC adopted some new advanced tools and the most prominent one is the flexible hierarchical coding structures which include coding unit (CU), prediction unit (PU), transform unit (TU). All of those units must be test through rate-distortion optimisation. Since the CU is highly content dependent it is not efficient to test all the modes. In this study, the authors propose a fast coding algorithm for HEVC based on video contents. The authors statistically analysis the features of video contents from three aspects, the pixel gradient, the block mean value, and the block variance of CU. Then, jointly use these features with the CU depth levels and the prediction modes of spatiotemporal adjacent CUs to realise fast CU depth level decision and fast prediction mode decision. The experimental results show that the proposed algorithm can save 58.9 and 57.6% computational complexity on average with only average 1.8 and 1.9% bitrate losses under ‘random access, main’ and ‘low-delay, main’ conditions, respectively.

Amarjit Roy;Joyeeta Singha;Lalit Manam;Rabul Hussain Laskar; "Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images," vol.11(6), pp.352-361, 6 2017. In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high-density impulse noise from colour images. In the proposed filtering scheme, the noisy and non-noisy pixels are classified based on the non-causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non-noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal-to-noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (∼1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high-density impulse noise.

Changkun Wu;Wei Zhang;Qi Jia;Yanyan Liu; "Hardware efficient multiplier-less multi-level 2D DWT architecture without off-chip RAM," vol.11(6), pp.362-369, 6 2017. This study presents a multi-level 2D discrete wavelet transform (DWT) architecture without off-chip RAM. Existing architectures use one off-chip RAM to store the image data, which increases the complexity of the system. For one-chip design, line-based architecture based on modified lifting scheme is proposed. By replacing the multipliers with canonic sign digit multipliers, a critical path of one full-adder delay is achieved. As per theoretical estimate, for three-level 2D DWT with an image of N × N size, the proposed architecture requires 123 adders, 66 subtracters, 167 registers, temporal memory of 7.5N words and input RAM of 3N bytes. The estimated hardware requirement shows that for the image size of 512 × 512 and three-level DWT, the proposed architecture involves at least 14.1% less transistor-delay-product than existing architectures.

Zhenqiu Shu;Hongfei Fan;Pu Huang;Dong Wu;Feiyue Ye;Xiaojun Wu; "Multiple Laplacian graph regularised low-rank representation with application to image representation," vol.11(6), pp.370-378, 6 2017. Recently, low-rank representation (LRR)-based techniques have manifested remarkable results for data representation. To exploit the latent manifold structure of data, the graph regulariser is incorporated into the model of LRR. However, it is critical to construct an appropriate graph model and set the corresponding parameters. In addition, this procedure is usually time-consuming and proved to be overfitting when using cross validation or discrete grid search. Two novel LRR-based methods, called multiple graph regularised LRR and multiple hypergraph regularised LLR, are proposed to represent the high-dimensional data. To guarantee the smoothness along the estimated manifold, the multiple graph regulariser and the multiple hypergraph regulariser are incorporated into the traditional LRR method, respectively, which results in a unified framework. Moreover, the augmented Lagrange multiplier is adopted to solve the proposed models. Extensive experiments on real image datasets show the effectiveness of the proposed methods.

Shizhong Li;Haibing Yin;Xiangzhong Fang;Huijuan Lu; "Lossless image compression algorithm and hardware architecture for bandwidth reduction of external memory," vol.11(6), pp.379-388, 6 2017. In high definition (HD) video coders, huge memory access bandwidth is the major throughput bottleneck. Lossless embedded compression is an efficient solution to alleviate the bandwidth burden, in which image are compressed before writing into local memory and decompressed after retrieving from local memory. This study proposes a hardware-oriented lossless image compression algorithm, supporting block and line random access flexibly for adapting diverse hardware video codec architectures. The major contributions are characterised as follows. First, block or pixel-level adaptive prediction is proposed to fully utilise the image spatial correlation by employing adaptive mode decision. Second, multiple-range semi-fixed (SF) variable length coding (VLC) is employed to describe the prediction residue, and adaptive block size selection is employed for SF VLC to fully utilise the statistical redundancy. In addition, Huffman VLC is further employed to represent the control syntax elements. Third, four-stage pipeline hardware architecture is proposed to implement the proposed algorithm. Simulation results show that the proposed algorithm achieves competitive rate compression performance compared with reference algorithms. The proposed hardware architecture is verified supporting real-time processing for quad-HD videos at the frequency of 166 MHz. The proposed work achieves reducing memory access bandwidth by ∼55.2%, which is useful for hardwired video coding.

Abdelghani Tafsast;Mohamed Laid Hadjili;Ayache Bouakaz;Nabil Benoudjit; "Unsupervised cluster-based method for segmenting biological tumour volume of laryngeal tumours in 18F-FDG-PET images," vol.11(6), pp.389-396, 6 2017. In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in 18F-FDG-PET images. The technique is based on the k-means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7%) compared with other existing methods in clinical routine, including fuzzy c-means with (35.58%), gradient-based method with (19.14%) and threshold-based methods.

Mahdi Nasiri;Mohammad Reza Mosavi;Sattar Mirzakuchaki; "IR small target detection based on human visual attention using pulsed discrete cosine transform," vol.11(6), pp.397-405, 6 2017. Detection of small targets in an infrared (IR) image with high reliability is very important for defence systems. Small targets in an IR image are defined as salient features which attract the attention of human visual system. In this study, a robust method for detection of small targets in an IR image is proposed based on HV attention. In this method, first, the Gaussian-like feature maps are extracted from the original image. Then, saliency maps (SMs) are created based on pulsed discrete cosine transform, in which the target is salient and background clutter is suppressed. Finally, to increase the contrast between target and background clutter and to raise robustness of this method against false alarms, SMs are fused adaptively. Experiments are carried out on the data set including real-life IR images with small targets as well as various and complicated backgrounds. Qualitative and quantitative assessments show that the proposed method can detect small targets in IR image with high reliability and is more effective compared with other methods based on HV attention. Therefore, it can be used in many applications for detection of small targets in IR image with minimum false alarms.

Yong Guo;Bing-Zhao Li;Navdeep Goel; "Optimised blind image watermarking method based on firefly algorithm in DWT-QR transform domain," vol.11(6), pp.406-415, 6 2017. Firefly algorithm (FA) is one of the newly developed nature inspired optimisation algorithm, inspired by the flashing behaviour of fireflies that a firefly tends to be attracted towards other fireflies with higher brightness. Thus FA has two advantages: local attractions and automatic regrouping. Based on these good properties, a novel image watermarking method based on FA in discrete wavelet transform (DWT)-QR transform domain is proposed in this study. Structural similarity index measure and bit error rate are used in the objective function to trade-off invisibility and robustness. The experiment results show that the proposed image watermarking method not only meet the need of invisibility, but also has better or comparable robustness as compared with some related methods.

Chunyan Shao;Qinghai Ding;Haibo Luo;Zheng Chang;Chi Zhang;Tianjiang Zheng; "Step-by-step pipeline processing approach for line segment detection," vol.11(6), pp.416-424, 6 2017. This study proposes a line segment detection that can efficiently and effectively handle non-linear uniform intensity changes. The presented sketching algorithm applies the resistant to affine transformation and monotonic intensity change (RATMIC) descriptor to conduct binary translation in the image pre-processing step, which can remove the unwanted smoothing of the Canny detector in most line detections. The Harris corner detector is applied to catch regions of line segments for the purpose of simulating the composition of sketching and achieving a sense of unity within the picture. Furthermore, the RATMIC descriptor is employed to obtain binary images of the regions of interest (ROIs). Finally, small eigenvalue analysis is implemented to detect straight lines in the ROIs. The experiments conducted on various images with image rotation, scaling, and translation validate the effectiveness of the proposed method. The experimental results also demonstrate that about 30% in the overall coverage of major lines and 20% in the coverage per major line are increased compared with the state-of-the-art line detectors. Moreover, the performance of the proposed method produces a combined advantage of ∼17% in the coverage of line segments over the line segment detector with noisy images.

Zelong Wang;Xintong Tan;Qi Yu;Jubo Zhu; "Sparse PDE for SAR image speckle suppression," vol.11(6), pp.425-432, 6 2017. Speckle suppression is extremely important for understanding and utilising synthetic aperture radar (SAR) images, while the emphasis of the traditional methods for speckle suppression is usually focused on removing the noise instead of keeping the scattering character of imaging objects, which has caused serious interference to the subsequent applications of SAR images. The authors aim to develop sparse partial differential equation (PDE) for speckle suppression of SAR image, where the PDE model is associated with the sparse prior of objects and the statistical property of speckle. The PDE model has been proved to have the ability of denoising and edge-preserving by proper design, and the sparse prior of the point- and line-like objects on SAR images has been also illustrated, both of which help for keeping the scattering characters. To solve the proposed sparse PDE model, a numerical algorithm is designed and the sparse constraint is realised in each step of the diffusion process. In experiments, several real SAR images are utilised to validate the performance of the proposed method.

Achala Pandey;Umesh Chandra Pati; "Development of saliency-based seamless image compositing using hybrid blending (SSICHB)," vol.11(6), pp.433-442, 6 2017. With the advancement in computer vision and graphics tools, digital compositing has become an integral part of the present computer-generated visual effects. However, factors such as inaccurate mask generation, intensive user interaction, and the creation of boundary seams due to colour or texture difference, make it hard to achieve quality composites. Poisson editing efficiently generates seamless composites but results in undesirable colour bleeding. Multi-resolution blending, in contrast, produces colour consistent composites; however often introduces blurry boundaries around the source object inserted. Motivated by these observations, the authors propose a colour consistent seamless compositing pipeline by integrating two new approaches. First, the authors use a visual attention algorithm based on the colour difference with increased edge weight using Gaussian filter bank (GFB) to ensure the least user interaction during mask generation. Second, the authors propose a hybrid framework by incorporating the goodness of the two different blending methods namely modified Poisson editing (MPE) and GFB-based multi-resolution blending to create colour consistent seamless composites. An extensive experiment has been carried out on challenging datasets to validate the proposed technique. Comparison with the state-of-the-art techniques shows the efficacy of the authors’ algorithm in generating colour consistent, seamless, and natural-looking composites for present image editing applications.

Min Qin;Xiaoxin Lv;Xiaohui Chen;Weidong Wang; "Hybrid NSS features for no-reference image quality assessment," vol.11(6), pp.443-449, 6 2017. A novel general-purpose no-reference image quality assessment (NR-IQA) model utilising hybrid natural scene statistics (HNSS) is proposed. Distinguished from existing NR-IQA approaches, the new model combines the statistics of locally mean subtracted and contrast normalised coefficients in the spatial domain and the statistics of image patch coefficients in a codebook space, which is constructed by codebooks extracted from pristine images using K-Means. The authors demonstrate that the coefficients in the codebook space keep the NSS characteristics as same as these in the spatial domain. After extracting the statistical features, a two-stage framework of distortion classification followed by quality assessment is applied. Experimental results show that the authors’ predicted quality score well matches human perceptual image quality. The proposed model outperforms state-of-the-art general-purpose NR-IQA approaches when it is tested on the LIVE database.

IEEE Transactions on Signal Processing - new TOC (2017 June 19) [Website]

Xiao Fu;Kejun Huang;Mingyi Hong;Nicholas D. Sidiropoulos;Anthony Man-Cho So; "Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis," vol.65(16), pp.4150-4165, Aug.15, 15 2017. Generalized canonical correlation analysis (GCCA) aims at finding latent low-dimensional common structure from multiple views (feature vectors in different domains) of the same entities. Unlike principal component analysis that handles a single view, (G)CCA is able to integrate information from different feature spaces. Here we focus on MAX-VAR GCCA, a popular formulation that has recently gained renewed interest in multilingual processing and speech modeling. The classic MAX-VAR GCCA problem can be solved optimally via eigen-decomposition of a matrix that compounds the (whitened) correlation matrices of the views; but this solution has serious scalability issues, and is not directly amenable to incorporating pertinent structural constraints such as nonnegativity and sparsity on the canonical components. We posit regularized MAX-VAR GCCA as a nonconvex optimization problem and propose an alternating optimization-based algorithm to handle it. Our algorithm alternates between inexact solutions of a regularized least squares subproblem and a manifold-constrained nonconvex subproblem, thereby achieving substantial memory and computational savings. An important benefit of our design is that it can easily handle structure-promoting regularization. We show that the algorithm globally converges to a critical point at a sublinear rate, and approaches a global optimal solution at a linear rate when no regularization is considered. Judiciously designed simulations and large-scale word embedding tasks are employed to showcase the effectiveness of the proposed algorithm.

Zbyněk Koldovský;Francesco Nesta; "Performance Analysis of Source Image Estimators in Blind Source Separation," vol.65(16), pp.4166-4176, Aug.15, 15 2017. Blind methods often separate or identify signals or signal subspaces up to an unknown scaling factor. Sometimes it is necessary to cope with the scaling ambiguity, which can be done through reconstructing signals as they are received by sensors, because scales of the sensor responses (images) have known physical interpretations. In this paper, we analyze two approaches that are widely used for computing the sensor responses, especially, in frequency-domain independent component analysis. One approach is the least-squares projection, whereas the other one assumes a regular mixing matrix and computes its inverse. Both estimators are invariant to the unknown scaling. Although frequently used, their differences were not studied yet. A goal of this paper is to fill this gap. The estimators are compared through a theoretical study, perturbation analysis, and simulations. We point to the fact that the estimators are equivalent when the separated signal subspaces are orthogonal, and vice versa. Two applications are shown, one of which demonstrates a case where the estimators yield substantially different results.

Shiqi Gong;Chengwen Xing;Sheng Chen;Zesong Fei; "Secure Communications for Dual-Polarized MIMO Systems," vol.65(16), pp.4177-4192, Aug.15, 15 2017. To enhance secure communications, we deploy the dual-polarized antenna arrays at communication nodes of the multi-input multioutput (MIMO) system, where the base station communicates with multiple legitimate users in the presence of an eavesdropper. We also adopt the dual-structured precoding in which a preprocessing matrix based on the polarized array spatial correlation and a linear precoding based on the instantaneous channel state information (CSI) are concatenated. We design this dual-structured multiuser linear precoding under three cases. In the first case, given perfect global CSI, the secrecy rate optimization problem is formulated and transformed into the weighted minimum mean square error (MSE) problem, which can be effectively solved by the block coordinate decent method. In the second case, where the eavesdropper's CSI is unavailable, an artificial noise is generated to confuse the eavesdropper by minimizing the information transmit power subject to a preset MSE threshold for the recovered confidential signals, which can be solved by an efficient iterative algorithm. In the third case of imperfect global CSI, the robust optimization for secure communications is performed by minimizing the largest received MSE among the users subject to the total transmit power constraint, which can be reformulated into a biconvex semidefinite programming problem and solved by an efficient alternating convex optimization. Simulation results are included to demonstrate the excellent performance of our proposed designs over the conventional single-polarized array-based designs, in terms of achievable secrecy rate, minimum transmit power, and the MSE of recovered confidential signals.

Paolo Di Lorenzo;Paolo Banelli;Sergio Barbarossa;Stefania Sardellitti; "Distributed Adaptive Learning of Graph Signals," vol.65(16), pp.4193-4208, Aug.15, 15 2017. The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean-square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs.

Mohammadreza Soltani;Chinmay Hegde; "Fast Algorithms for Demixing Sparse Signals From Nonlinear Observations," vol.65(16), pp.4209-4222, Aug.15, 15 2017. We study the problem of demixing a pair of sparse signals from noisy, nonlinear observations of their superposition. Mathematically, we consider a nonlinear signal observation model, <inline-formula> <tex-math notation="LaTeX">$y_i = g(a_i^Tx) + e_i, \ i=1,\ldots,m$</tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$x = \Phi w+\Psi z$</tex-math></inline-formula> denotes the superposition signal, <inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\Psi$</tex-math></inline-formula> are orthonormal bases in <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^n$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX"> $w, z\in \mathbb {R}^n$</tex-math></inline-formula> are sparse coefficient vectors of the constituent signals, and <inline-formula><tex-math notation="LaTeX">$e_i$</tex-math></inline-formula> represents the noise. Moreover, <inline-formula><tex-math notation="LaTeX">$g$</tex-math></inline-formula> represents a nonlinear link function, and <inline-formula><tex-math notation="LaTeX">$a_i\in \mathbb {R}^n$</tex-math></inline-formula> is the <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula>th row of the measurement matrix <inline-formula><tex-math notation="LaTeX">$A\in \mathbb {R}^{m\times n}$</tex-math></inline-formula>. Problems of this nature arise in several applications ranging from astronomy, computer vision, and machine learning. In this paper, we make some concrete algorithmic progress for the above demixing problem. Specifically, we consider two scenarios: first, the case when the demixing procedure has no knowledge of the link function, and second is the case when the demixing algorithm has perfect knowledge of the link function. In both cases, we provide fast algorithms for recovery of the constituents <inline-formula><tex-math notation="LaTeX">$w$<- tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$z$</tex-math></inline-formula> from the observations. Moreover, we support these algorithms with a rigorous theoretical analysis and derive (nearly) tight upper bounds on the sample complexity of the proposed algorithms for achieving stable recovery of the component signals. We also provide a range of numerical simulations to illustrate the performance of the proposed algorithms on both real and synthetic signals and images.

Graham W. Pulford; "Channel-Dependent Constrained Combinatorial Clustering," vol.65(16), pp.4223-4237, Aug.15, 15 2017. Constrained combinatorial clustering (CCC) is a new approach for grouping multiple features where the clustering metric depends on an unknown communication channel assignment. Features assigned to the same channel cannot be from the same source and, conversely, channels assigned to the same source must be distinct. While the number of sources and their states are unknown, the channels are assumed to be known except for additive noise. Potential clustering assignments are checked for compatibility with the constraints in a structured way that results in significant computational savings with respect to exhaustive enumeration, especially when combined with a <inline-formula> <tex-math notation="LaTeX">$K$</tex-math></inline-formula>-best channel assignment algorithm that has polynomial complexity. By combining two channel assignment methods (exhaustive and <inline-formula><tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-best) with two clustering techniques (CCC and greedy), four new algorithms are presented to solve this novel problem, along with detailed computational complexity analyses. The main algorithm (CCC) is a top-down clustering strategy based on assignment aggregation in a channel constrained environment. The approaches are compared on a one-dimensional simulation. Significant performance differences in source estimation accuracy and estimated number of sources are observed at low signal-to-noise ratios (SNRs) and for low values of <inline-formula> <tex-math notation="LaTeX">$K$</tex-math></inline-formula>. Some novel permutation symmetry properties arising from the study, which lead to a new type of self-affine set or discrete fractal, are also presented.

İlker Bayram;Savaşkan Bulek; "A Penalty Function Promoting Sparsity Within and Across Groups," vol.65(16), pp.4238-4251, Aug.15, 15 2017. We introduce a new penalty function that promotes signals composed of a small number of active groups, where within each group, only a few high magnitude coefficients are nonzero. We derive the threshold function associated with the proposed penalty and study its properties. We discuss how the proposed penalty/threshold function can be useful for signals with isolated nonzeros, such as audio with isolated harmonics along the frequency axis, or reflection functions in exploration seismology where the nonzeros occur on the boundaries of subsoil layers. We demonstrate the use of the proposed penalty/threshold functions in a convex denoising and a nonconvex deconvolution formulation. We provide convergent algorithms for both formulations and compare the performance with state-of-the-art methods.

Panos P. Markopoulos;Sandipan Kundu;Shubham Chamadia;Dimitris A. Pados; "Efficient L1-Norm Principal-Component Analysis via Bit Flipping," vol.65(16), pp.4252-4264, Aug.15, 15 2017. It was shown recently that the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> L1-norm principal components (L1-PCs) of a real-valued data matrix <inline-formula><tex-math notation="LaTeX">$\mathbf X \in \mathbb {R}^{D \times N}$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$N$</tex-math> </inline-formula> data samples of <inline-formula><tex-math notation="LaTeX">$D$</tex-math></inline-formula> dimensions) can be exactly calculated with cost <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(2^{NK})$ </tex-math></inline-formula> or, when advantageous, <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N^{dK - K + 1})$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$d=\mathrm{rank}(\mathbf X)$ </tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$K<d$</tex-math></inline-formula>. In applications where <inline-formula><tex-math notation="LaTeX">$\mathbf X$</tex-math></inline-formula> is large (e.g., “big” data of large <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> and/or “heavy” data of large <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula>), these costs are prohibitive. In this paper, we present a novel suboptimal algorithm for the calculation of the <inline-formula><tex-math notation="LaTeX">$K < d$</tex-math></inline-formula> L1-PCs of <inline-formula> <tex-math notation="LaTeX">$\mathbf X$</tex-math></inline-formula> of cost <inline-formula><tex-math notation="LaTeX"> $\mathcal O (ND \mathrm{min} \lbrace N,D\rbrace + N^2K^2(K^2 + d))$</tex-math></inline-formula>, which is comparable to that of standard L2-norm PC analysis. Our theoretical and experimental studies show that the proposed algorithm calculates the exact optimal L1-PCs with high frequency and achieves higher value - n the L1-PC optimization metric than any known alternative algorithm of comparable computational cost. The superiority of the calculated L1-PCs over standard L2-PCs (singular vectors) in characterizing potentially faulty data/measurements is demonstrated with experiments in data dimensionality reduction and disease diagnosis from genomic data.

Jure Sokolić;Raja Giryes;Guillermo Sapiro;Miguel R. D. Rodrigues; "Robust Large Margin Deep Neural Networks," vol.65(16), pp.4265-4280, Aug.15, 15 2017. The generalization error of deep neural networks via their classification margin is studied in this paper. Our approach is based on the Jacobian matrix of a deep neural network and can be applied to networks with arbitrary nonlinearities and pooling layers, and to networks with different architectures such as feed forward networks and residual networks. Our analysis leads to the conclusion that a bounded spectral norm of the network's Jacobian matrix in the neighbourhood of the training samples is crucial for a deep neural network of arbitrary depth and width to generalize well. This is a significant improvement over the current bounds in the literature, which imply that the generalization error grows with either the width or the depth of the network. Moreover, it shows that the recently proposed batch normalization and weight normalization reparametrizations enjoy good generalization properties, and leads to a novel network regularizer based on the network's Jacobian matrix. The analysis is supported with experimental results on the MNIST, CIFAR-10, LaRED, and ImageNet datasets.

IEEE Signal Processing Letters - new TOC (2017 June 19) [Website]

Ki-Seung Lee; "Restricted Boltzmann Machine-Based Voice Conversion for Nonparallel Corpus," vol.24(8), pp.1103-1107, Aug. 2017. A large amount of parallel training corpus is necessary for robust, high-quality voice conversion. However, such parallel data may not always be available. This letter presents a new voice conversion method that needs no parallel speech corpus, and adopts a restricted Boltzmann machine (RBM) to represent the distribution of the spectral features derived from a target speaker. A linear transformation was employed to convert the spectral and delta features. A conversion function was obtained by maximizing the conditional probability density function with respect to the target RBM. A feasibility test was carried out on the OGI VOICES corpus. Results from the subjective listening tests and the objective results both showed that the proposed method outperforms the conventional GMM-based method.

Shunsuke Ono; "Primal-Dual Plug-and-Play Image Restoration," vol.24(8), pp.1108-1112, Aug. 2017. We propose a new plug-and-play image restoration method based on primal-dual splitting. Existing plug-and-play image restoration methods interpret any off-the-shelf Gaussian denoiser as one step of the so-called alternating direction method of multipliers (ADMM). This makes it possible to exploit the power of such a highly-customized Gaussian denoising method for general image restoration tasks in a plug-and-play fashion. However, the ADMM-based plug-and-play approach (ADMMPnP) has several limitations: 1) it often requires a problem-specific iterative method in solving a subproblem, which results in a computationally expensive inner loop; and 2) it is specialized to handle the formulation of a regularization (plug-and-play) term plus a data-fidelity term, so that it does not allow to impose hard constraints useful for image restoration. Our approach resolves these issues by leveraging the nature of primal-dual splitting, yielding a very flexible plug-and-play image restoration method. Experimental results demonstrate that the proposed method is much more efficient than ADMMPnP with an inner loop, whereas it keeps the same efficiency as ADMMPnP in the case where the subproblem of ADMMPnP can be solved efficiently.

Maoxin Tian;Qi Zhang;Sai Zhao;Quanzhong Li;Jiayin Qin; "Secrecy Sum Rate Optimization for Downlink MIMO Nonorthogonal Multiple Access Systems," vol.24(8), pp.1113-1117, Aug. 2017. Nonorthogonal multiple access (NOMA) is expected to be a promising technique for future wireless networks. In this letter, we investigate the secrecy sum rate optimization problem for a downlink multiple-input-multiple-output NOMA system that consists of a base station, multiple legitimate users, and an eavesdropper. Our objective is to maximize achievable secrecy sum rate subject to successful successive interference cancellation constraints and transmit power constraint. The formulated optimization problem is nonconvex. Motivated by the relationship between mutual information rate and minimum mean square error, we propose to transform the secrecy sum rate optimization problem into a biconvex problem. The biconvex problem is solved by alternating optimization method where in each iteration, we solve a second-order cone programming. Simulation results demonstrate that our proposed NOMA scheme outperforms conventional orthogonal multiple access scheme.

Pin-Yu Chen;Sijia Liu; "Bias-Variance Tradeoff of Graph Laplacian Regularizer," vol.24(8), pp.1118-1122, Aug. 2017. This letter presents a bias-variance tradeoff of graph Laplacian regularizer, which is widely used in graph signal processing and semisupervised learning tasks. The scaling law of the optimal regularization parameter is specified in terms of the spectral graph properties and a novel signal-to-noise ratio parameter, which suggests that selecting a mediocre regularization parameter is often suboptimal. The analysis is applied to three applications, including random, band-limited, and multiple-sampled graph signals. Experiments on synthetic and real-world graphs demonstrate near-optimal performance of the established analysis.

Frank Nielsen;Richard Nock; "Generalizing Skew Jensen Divergences and Bregman Divergences With Comparative Convexity," vol.24(8), pp.1123-1127, Aug. 2017. Comparative convexity is a generalization of ordinary convexity based on abstract means instead of arithmetic means. We introduce the generalized skew Jensen divergences and their corresponding Bregman divergences with respect to comparative convexity. To illustrate those novel families of divergences, we consider the convexity induced by quasi-arithmetic means, and report explicit formula for the corresponding Bregman divergences. In particular, we show that those new Bregman divergences are equivalent to conformal ordinary Bregman divergences on monotone embeddings, and further state related results.

Syed Shahnawazuddin;Rohit Sinha;Gayadhar Pradhan; "Pitch-Normalized Acoustic Features for Robust Children's Speech Recognition," vol.24(8), pp.1128-1132, Aug. 2017. In this letter, the effectiveness of recently reported SMAC (Spectral Moment time–frequency distribution Augmented by low-order Cepstral) features has been evaluated for robust automatic speech recognition (ASR). The SMAC features consist of normalized first central spectral moments appended with low-order cepstral coefficients. These features have been designed for achieving robustness to both additive noise and the pitch variations. We have explored the SMAC features in severe pitch mismatch ASR task, i.e., decoding of children's speech on adults’ speech trained ASR system. In those tasks, the SMAC features are still observed to be sensitive to pitch variations. Toward addressing the same, a simple spectral smoothening approach employing adaptive-cepstral truncation is explored prior to the computation of spectral moments. With the proposed modification, the SMAC features are noted to achieve enhanced pitch robustness without affecting their noise immunity. Furthermore, the effectiveness of the proposed features is explored in three dominant acoustic modeling paradigms and varying data conditions. In all the cases, the proposed features are observed to significantly outperform the existing ones.

M. Kiran Reddy;K. Sreenivasa Rao; "Robust Pitch Extraction Method for the HMM-Based Speech Synthesis System," vol.24(8), pp.1133-1137, Aug. 2017. This letter proposes an efficient method for extracting pitch from speech signals for the hidden Markov model (HMM)-based speech synthesis system (HTS). In the proposed method, voicing detection and pitch estimation is performed using the mean signal obtained from continuous wavelet transform coefficients. The proposed pitch extraction method is integrated in the HMM-based speech synthesis system. The Performance of the proposed method is evaluated on CMU Arctic and Keele databases. Both objective and subjective evaluation results show that the quality of speech synthesized with the proposed pitch estimation method is much better compared with HMM-based speech synthesis systems developed using the state-of-the-art pitch extraction methods, namely, robust algorithm for pitch tracking and speech transformation and representation using adaptive interpolation of weighted spectrum employed in the HTS.

Lakshmi Krishnan;Terence Betlehem;Paul D. Teal; "A Statistically Robust Approach to Acoustic Impulse Response Shaping," vol.24(8), pp.1138-1142, Aug. 2017. Acoustic impulse response shaping is a prefiltering technique to reduce the perceptible effects of room reverberation. The room impulse responses to be shaped must first be measured, and these measurements can contain errors. Furthermore, room responses vary with changes in temperature and humidity and also with changes in measurement position. This letter presents a method for enhancing the robustness of the shaping procedure so that the effects of these errors and changes are minimized. The method uses a stochastic model of the channel variations to explicitly limit the probability of large deviations from the desired performance. The method is evaluated on realistic channel perturbations and the resulting shaped responses are shown to comply with the robustness specification.

IEEE Journal of Selected Topics in Signal Processing - new TOC (2017 June 19) [Website]

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Junichi Yamagishi;Tomi H. Kinnunen;Nicholas Evans;Phillip De Leon;Isabel Trancoso; "Introduction to the Issue on Spoofing and Countermeasures for Automatic Speaker Verification," vol.11(4), pp.585-587, June 2017. The papers in this special issue focus on automatic speaker verification (ASV) technologies and applications for their use. ASV offers a low-cost and flexible solution to biometric authentication. While there liability of ASV systems is now considered sufficient to support mass-market adoption, there are concerns that the technology is vulnerable to spoofing, also referred to as presentation attacks. Spoofing refers to an attack whereby a fraudster attempts to manipulate a biometric system by masquerading as another, enrolled person. Replayed, synthesized and converted speech spoofing attacks can all be used to present high-quality, convincing speech signals which are representative of other, specific speakers and thus present a genuine threat to the reliability of ASV authentication systems.

Zhizheng Wu;Junichi Yamagishi;Tomi Kinnunen;Cemal Hanilçi;Mohammed Sahidullah;Aleksandr Sizov;Nicholas Evans;Massimiliano Todisco; "ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge," vol.11(4), pp.588-604, June 2017. Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence of competitive evaluations and the lack of common datasets has hampered progress in developing effective spoofing countermeasures. This paper describes the ASV Spoofing and Countermeasures (ASVspoof) initiative, which aims to fill this void. Through the provision of a common dataset, protocols, and metrics, ASVspoof promotes a sound research methodology and fosters technological progress. This paper also describes the ASVspoof 2015 dataset, evaluation, and results with detailed analyses. A review of postevaluation studies conducted using the same dataset illustrates the rapid progress stemming from ASVspoof and outlines the need for further investigation. Priority future research directions are presented in the scope of the next ASVspoof evaluation planned for 2017.

Dipjyoti Paul;Monisankha Pal;Goutam Saha; "Spectral Features for Synthetic Speech Detection," vol.11(4), pp.605-617, June 2017. Recent advancements in voice conversion (VC) and speech synthesis research make speech-based biometric systems highly prone to spoofing attacks. This can provoke an increase in false acceptance rate in such systems and requires countermeasure to mitigate such spoofing attacks. In this paper, we first study the characteristics of synthetic speech vis-à-vis natural speech and then propose a set of novel short-term spectral features that can efficiently capture the discriminative information between them. The proposed features are computed using inverted frequency warping scale and overlapped block transformation of filter bank log energies. Our study presents a detailed analysis of antispoofing performance with respect to the variations in the warping scale for inverted frequency and block size for the block transform. For performance analysis, Gaussian mixture model (GMM) based synthetic speech detector is used as a classifier on a stand-alone basis and also, integrated with automatic speaker verification (ASV) systems. For ASV systems, standard mel-frequency cepstral coefficients are used as feature while GMM with universal background model and i-vector are used as classifiers. The experiments are conducted on ten different kinds of synthetic data from ASVspoof 2015 corpus. The results show that the countermeasures based on the proposed features outperform other spectral features for both known and unknown attacks. An average equal error rate (EER) of 0.00% has been achieved for nine attacks that use VC or SS speech and the best performance of 7.12% EER is arrived at the remaining natural speech concatenation-based spoofing attack.

Tanvina B. Patel;Hemant A. Patil; "Cochlear Filter and Instantaneous Frequency Based Features for Spoofed Speech Detection," vol.11(4), pp.618-631, June 2017. Vulnerability of voice biometrics systems to spoofing attacks by synthetic speech (SS) and voice converted (VC) speech has arose the need of standalone spoofed speech detection (SSD) systems. This paper is an extension of our previously proposed features (used in relatively best performing SSD system) at the first ASVspoof 2015 challenge held at INTERSPEECH 2015. For the challenge, the authors proposed novel features based on cochlear filter cepstral coefficients (CFCC) and instantaneous frequency (IF), i.e., CFCCIF. The basic motivation behind this is that human ear processes speech in subbands. The envelope of each subband and its IF is important for perception of speech. In addition, the transient information also adds to the perceptual information that is captured. We observed that subband energy variations across CFCCIF when estimated by symmetric difference (CFCCIFS) gave better discriminative properties than CFCCIF. The features are extracted at frame level and the Gaussian mixture model based classification system was used. Experiments were conducted on ASVspoof 2015 challenge database with MFCC, CFCC, CFCCIF, and CFCCIFS features. On the evaluation dataset, after score-level fusion with MFCC, the CFCCIFS features gave an overall equal error rate (EER) of 1.45% as compared to 1.87% and 1.61% with CFCCIF and CFCC, respectively. In addition to detecting the known and unknown attacks, intensive experiments have been conducted to study the effectiveness of the features under the condition that either only SS or only VC speech is available for training. It was observed that when only VC speech is used in training, both VC, as well as SS, can be detected. However, when only SS is used in training, VC speech was not detected. In general, amongst vocoder-based spoofs, it was observed that VC speech is relatively difficult to detect than SS by the SSD system. However, vocoder-independent SS was toughest with h- ghest EER (i.e., > 10%).

Kaavya Sriskandaraja;Vidhyasaharan Sethu;Eliathamby Ambikairajah;Haizhou Li; "Front-End for Antispoofing Countermeasures in Speaker Verification: Scattering Spectral Decomposition," vol.11(4), pp.632-643, June 2017. As speaker verification is widely used as a means of verifying personal identity in commercial applications, the study of antispoofing countermeasures has become increasingly important. By choosing appropriate spectral and prosodic feature mapping, spoofing methods based on voice conversion and speech synthesis are both capable of deceiving speaker verification systems that typically rely on these features. Consequently alternative front-ends are required for effective spoofing detection. This paper investigates the use of the recently proposed hierarchical scattering decomposition technique, which can be viewed as a generalization of all constant-Q spectral decompositions, to implement front-ends for stand-alone spoofing detection. The coefficients obtained using this decomposition are converted to a feature vector of Scattering Cepstral Coefficients (SCCs). We evaluate the performance of SCCs on the recent spoofing and Antispoofing (SAS) corpus as well as the ASVspoof 2015 challenge corpus and show that SCCs are superior to all other front-ends that have previously been benchmarked on the ASVspoof corpus.

Tanvina B. Patel;Hemant A. Patil; "Significance of Source–Filter Interaction for Classification of Natural vs. Spoofed Speech," vol.11(4), pp.644-659, June 2017. Countermeasures used to detect synthetic and voice-converted spoofed speech are usually based on excitation source or system features. However, in the natural speech production mechanism, there exists nonlinear source–filter (S–F) interaction as well. This interaction is an attribute of natural speech and is rarely present in synthetic or voice-converted speech. Therefore, we propose features based on the S–F interaction for a spoofed speech detection (SSD) task. To that effect, we estimate the voice excitation source (i.e., differenced glottal flow waveform, <inline-formula><tex-math notation="LaTeX">$\dot{g} (t))$</tex-math></inline-formula> and model it using the well-known Liljencrants–Fant model to get coarse structure, <inline-formula><tex-math notation="LaTeX"> $g_{c}(t)$</tex-math></inline-formula>. The residue or difference, <inline-formula><tex-math notation="LaTeX"> $g_{r}(t)$</tex-math></inline-formula>, between <inline-formula><tex-math notation="LaTeX">$\dot{g} (t)$</tex-math> </inline-formula> and <inline-formula><tex-math notation="LaTeX">$g_{c}(t)$</tex-math></inline-formula> is known to capture the nonlinear S–F interaction. In the time domain, the <inline-formula><tex-math notation="LaTeX"> $L^{2}$</tex-math></inline-formula> norm of <inline-formula><tex-math notation="LaTeX">$g_{r}(t)$</tex-math> </inline-formula> in the closed, open, and return phases of the glottis are considered as features. In the frequency domain, the Mel representation of <inline-formula><tex-math notation="LaTeX">$g_{r}(t)$</tex-math></inline-formula> showed significant contribution in the SSD task. The proposed features are evaluated on the first ASVspoof 2015 challenge database using a Gaussian mixture model based classification system. On the evaluation set, for vocoder-based spoofs (i.e., S1S9), the score-level fusion o- residual energy features, Mel representation of the residual signal, and Mel frequency cepstral coefficients (MFCC) features gave an equal error rate (EER) of 0.017%, which is much less than the 0.319% obtained with MFCC alone. Furthermore, the residues of the spectrogram (as well as the Mel-warped spectrogram) of estimated <inline-formula> <tex-math notation="LaTeX">$\dot{g} (t)$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX"> $g_{c}(t)$</tex-math></inline-formula> are also explored as features for the SSD task. The features are evaluated for robustness in the presence of additive white, babble, and car noise at various signal-to-noise-ratio levels on the ASVspoof 2015 database and for channel mismatch condition on the Blizzard Challenge 2012 dataset. For both cases, the proposed features gave significantly less EER than that obtained by MFCC on the evaluation set.

Longbiao Wang;Seiichi Nakagawa;Zhaofeng Zhang;Yohei Yoshida;Yuta Kawakami; "Spoofing Speech Detection Using Modified Relative Phase Information," vol.11(4), pp.660-670, June 2017. The detection of human and spoofing (synthetic or converted) speech has started to receive an increasing amount of attention. In this paper, modified relative phase (MRP) information extracted from a Fourier spectrum is proposed for spoofing speech detection. Because original phase information is almost entirely lost in spoofing speech using current synthesis or conversion techniques, some phase information extraction methods, such as the modified group delay feature and cosine phase feature, have been shown to be effective for detecting human speech and spoofing speech. However, existing phase information-based features cannot obtain very high spoofing speech detection performance because they cannot extract precise phase information from speech. Relative phase (RP) information, which extracts phase information precisely, has been shown to be effective for speaker recognition. In this paper, RP information is applied to spoofing speech detection, and it is expected to achieve better spoofing detection performance. Furthermore, two modified processing techniques of the original RP, that is, pseudo pitch synchronization and linear discriminant analysis based full-band RP extraction, are proposed in this paper. In this study, MRP information is also combined with the Mel-frequency cepstral coefficient (MFCC) and modified group delay. The proposed method was evaluated using the ASVspoof 2015: Automatic Speaker Verification Spoofing and Countermeasures Challenge dataset. The results show that the proposed MRP information significantly outperforms the MFCC, modified group delay, and other phase information based features. For the development dataset, the equal error rate (EER) was reduced from 1.883% of the MFCC, 0.567% of the modified group delay to 0.013% of the MRP. By combining the RP with the MFCC and modified group delay, the EER was reduced to 0.003%. For the evaluation dataset, the MRP o- tained much better performance than the magnitude-based feature and other phase-based features, except for S10 spoofing speech.

Cenk Demiroglu;Osman Buyuk;Ali Khodabakhsh;Ranniery Maia; "Postprocessing Synthetic Speech With a Complex Cepstrum Vocoder for Spoofing Phase-Based Synthetic Speech Detectors," vol.11(4), pp.671-683, June 2017. State-of-the-art speaker verification systems are vulnerable to spoofing attacks. To address the issue, high-performance synthetic speech detectors (SSDs) for existing spoofing methods have been proposed. Phase-based SSDs that exploit the fact that most of the parametric speech coders use minimum-phase filters are particularly successful when synthetic speech is generated with a parametric vocoder. Here, we propose a new attack strategy to spoof phase-based SSDs with the objective of increasing the security of voice verification systems by enabling the development of more generalized SSDs. As opposed to other parametric vocoders, the complex cepstrum approach uses mixed-phase filters, which makes it an ideal candidate for spoofing the phase-based SSDs. We propose using a complex cepstrum vocoder as a postprocessor to existing techniques to spoof the speaker verification system as well as the phase-based SSDs. Once synthetic speech is generated with a speech synthesis or a voice conversion technique, for each synthetic speech frame, a natural frame is selected from a training database using a spectral distance measure. Then, complex cepstrum parameters of the natural frame are used for resynthesizing the synthetic frame. In the proposed method, complex cepstrum-based resynthesis is used as a postprocessor. Hence, it can be used in tandem with any synthetic speech generator. Experimental results showed that the approach is successful at spoofing four phase-based SSDs across nine parametric attack algorithms. Moreover, performance at spoofing the speaker verification system did not substantially degrade compared to the case when no postprocessor is employed.

Chunlei Zhang;Chengzhu Yu;John H. L. Hansen; "An Investigation of Deep-Learning Frameworks for Speaker Verification Antispoofing," vol.11(4), pp.684-694, June 2017. In this study, we explore the use of deep-learning approaches for spoofing detection in speaker verification. Most spoofing detection systems that have achieved recent success employ hand-craft features with specific spoofing prior knowledge, which may limit the feasibility to unseen spoofing attacks. We aim to investigate the genuine-spoofing discriminative ability from the back-end stage, utilizing recent advancements in deep-learning research. In this paper, alternative network architectures are exploited to target spoofed speech. Based on this analysis, a novel spoofing detection system, which simultaneously employs convolutional neural networks (CNNs) and recurrent neural networks (RNNs) is proposed. In this framework, CNN is treated as a convolutional feature extractor applied on the speech input. On top of the CNN processed output, recurrent networks are employed to capture long-term dependencies across the time domain. Novel features including Teager energy operator critical band autocorrelation envelope, perceptual minimum variance distortionless response, and a more general spectrogram are also investigated as inputs to our proposed deep-learning frameworks. Experiments using the ASVspoof 2015 Corpus show that the integrated CNN–RNN framework achieves state-of-the-art single-system performance. The addition of score-level fusion further improves system robustness. A detailed analysis shows that our proposed approach can potentially compensate for the issue due to short duration test utterances, which is also an issue in the evaluation corpus.

Pavel Korshunov;Sébastien Marcel; "Impact of Score Fusion on Voice Biometrics and Presentation Attack Detection in Cross-Database Evaluations," vol.11(4), pp.695-705, June 2017. Research in the area of automatic speaker verification (ASV) has been advanced enough for the industry to start using ASV systems in practical applications. However, these systems are highly vulnerable to spoofing or presentation attacks, limiting their wide deployment. Therefore, it is important to develop mechanisms that can detect such attacks, and it is equally important for these mechanisms to be seamlessly integrated into existing ASV systems for practical and attack-resistant solutions. To be practical, however, an attack detection should (i) have high accuracy, (ii) be well-generalized for different attacks, and (iii) be simple and efficient. Several audio-based presentation attack detection (PAD) methods have been proposed recently but their evaluation was usually done on a single, often obscure, database with limited number of attacks. Therefore, in this paper, we conduct an extensive study of eight state-of-the-art PAD methods and evaluate their ability to detect known and unknown attacks (e.g., in a cross-database scenario) using two major publicly available speaker databases with spoofing attacks: AVspoof and ASVspoof. We investigate whether combining several PAD systems via score fusion can improve attack detection accuracy. We also study the impact of fusing PAD systems (via parallel and cascading schemes) with two i-vector and inter-session variability based ASV systems on the overall performance in both bona fide (no attacks) and spoof scenarios. The evaluation results question the efficiency and practicality of the existing PAD systems, especially when comparing results for individual databases and cross-database data. Fusing several PAD systems can lead to a slightly improved performance; however, how to select which systems to fuse remains an open question. Joint ASV-PAD systems show a significantly increased resistance to the attacks at the expense of slightly degraded performance f- r bona fide scenarios.

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Min Wu; "Content Ecosystem: Serving Diverse Interests in Our Community [From the Editor[Name:_blank]]," vol.34(3), pp.3-13, May 2017. My discussions with a number of colleagues during the conference were about our IEEE Signal Processing Magazine (SPM), either to explore ideas for potential articles as inspired by technical talks and panels or to brainstorm how the magazine can help support the initiatives or activities of the IEEE Signal Processing Society (SPS). For this latter role, a notion of “content ecosystem” was coined in my meeting with chief editors of the SigPort Repository, Dr. Yan Lindsay Sun, and of the Resource Center (formerly SigView), Dr. John McAllister, respectively, and the SPS staff members on public outreach and publications.

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* "New Society Officers Elected for 2018 [Society News[Name:_blank]]," vol.34(3), pp.8-9, May 2017.* Presents a listing of SPS Society officers elected for 2018.

John Edwards; "New Directions in Navigation and Positioning: Signal processing-enabled technologies pinpoint people, places, and things [Special Reports[Name:_blank]]," vol.34(3), pp.10-13, May 2017. In an era of same-day product deliveries, interplanetary space probes, and autonomous vehicles, transporting something-or someone-from here to there quickly, directly, and precisely is becoming increasingly important. An array of navigation and positioning technologies are now available to help guide and locate vehicles, people, and almost endless number of objects. The satellite-based global positioning system (GPS), for instance, now lies at the heart of an almost endless array of location, navigation, timing, mapping, and tracking tools. Real-time location system (RTLS) technologies, meanwhile, rely on resources such as GPS, Wi-Fi, Bluetooth, near-field communication (NFC), and radio-frequency identification (RFID) to detect the current location of a target, which may be anything from a vehicle to an item in a manufacturing plant to a person.

Xiao-Ping Steven Zhang;Fang Wang; "Signal Processing for Finance, Economics, and Marketing: Concepts, framework, and big data applications," vol.34(3), pp.14-35, May 2017. Economic data and financial markets are intriguing to researchers working on data and quantitative models. With rapid growth of and increasing access to data in digital form, finance, economics, and marketing data are poised to become one of the most important and tangible big data applications, owing not only to the relatively clean organization and structure of the data but also to clear application objectives and market demands. However, data-related economic studies often have different viewpoints from signal processing (SP). Also, many fundamental economics and business problems have been well formulated and studied in both theory and practice. The knowledge of foundational finance and economic theories will help SP and data researchers avoid reinventing the wheel and develop meaningful and useful research in these areas.

Huseyin Hacihabiboglu;Enzo De Sena;Zoran Cvetkovic;James Johnston;Julius O. Smith III; "Perceptual Spatial Audio Recording, Simulation, and Rendering: An overview of spatial-audio techniques based on psychoacoustics," vol.34(3), pp.36-54, May 2017. Developments in immersive audio technologies have been evolving in two directions: physically motivated systems and perceptually motivated systems. Physically motivated techniques aim to reproduce a physically accurate approximation of desired sound fields by employing a very high equipment load and sophisticated, computationally intensive algorithms. Perceptually motivated techniques, however, aim to render only the perceptually relevant aspects of the sound scene by means of modest computational and equipment load. This article presents an overview of perceptually motivated techniques, with a focus on multichannel audio recording and reproduction, audio source and reflection culling, and artificial reverberators.

Zhiwei Xiong;Yueyi Zhang;Feng Wu;Wenjun Zeng; "Computational Depth Sensing : Toward high-performance commodity depth cameras," vol.34(3), pp.55-68, May 2017. In this article, we introduce this important concept and provide an overview of the latest representative techniques. By bringing together and analyzing interdisciplinary research from signal processing, computer vision, and optics communities, our goal is to shed light on the development of future commodity depth cameras, which is a potential great interest to a broad audience. Specifically, this article will focus mainly on the structured light approach, which provides a large degree of freedom for the design of depth cameras. A recent review of ToF cameras is given, and another on light-field cameras is given. Also, a comprehensive review on traditional structured light cameras can be found. This article will be complementary to these reviews.

Monica F. Bugallo;Angela M. Kelly; "Engineering Outreach: Yesterday, Today, and Tomorrow [SP Education[Name:_blank]]," vol.34(3), pp.69-100, May 2017. This article discusses the current landscape of outreach efforts in the United States to engage K-12 students in engineering. It then provides an overview of two programs run by the College of Engineering and Applied Sciences, and the Institute for Science, Technology, Engineering, and Mathematics Education at Stony Brook University (SBU) to promote student participation and interest in engineering. These efforts are aligned with the recently released Next Generation Science Standards (NGSS), which emphasize incorporating engineering design principles in K-12 science education. We describe two models, one in the form of an on-campus summer camp and the other as a series of after-school activities with both on-and off-campus offerings. These experiences are rarely available in K-12 schools and have the added benefit of exposing students to engineering faculty and researchers. The programs are focused on electrical and computer engineering with emphasis on signal and information processing and analysis and have hosted more than 200 students for the past six years.

C.-C. Jay Kuo; "The CNN as a Guided Multilayer RECOS Transform [Lecture Notes[Name:_blank]]," vol.34(3), pp.81-89, May 2017. There is a resurging interest in developing a neural-network-based solution to the supervised machine-learning problem. The convolutional neural network (CNN) will be studied in this lecture note. We introduce a rectified-correlations on a sphere (RECOS) [1] transform as a basic building block of CNNs. It consists of two main concepts: 1) data clustering on a sphere and 2) rectification. We then interpret a CNN as a network that implements the guided multilayer RECOS transform with two highlights. First, we compare the traditional single-layer and modern multilayer signal-analysis approaches, point out key areas that enable the multilayer approach, and provide a full explanation to the operating principle of CNNs. Second, we discuss how guidance is provided by labels through backpropagation (BP) in the training.

Rodrigo Capobianco Guido; "Effectively Interpreting Discrete Wavelet Transformed Signals [Lecture Notes[Name:_blank]]," vol.34(3), pp.89-100, May 2017. Following two decades of research focusing on the discrete wavelet transform (DWT) and driven by students' high level of questioning, I decided to write this essay on one of the most significant tools for time-frequency signal analysis. As it is widely applicable in a variety of fields, I invite readers to follow this lecture note, which is specially dedicated to show a practical strategy for the interpretation of DWT-based transformed signals while extracting useful information from them. The particular focus resides on the procedure used to find the time support of frequencies and how it is influenced by the wavelet family and the support size of corresponding filters.

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Jorgen Jensen;Carlos Armando Villagomez Hoyos;Simon Holbek;Kristoffer Lindskov Hansen; "Velocity Estimation in Medical Ultrasound [Life Sciences[Name:_blank]]," vol.34(3), pp.94-100, May 2017. This article describes the application of signal processing in medical ultrasound velocity estimation. Special emphasis is on the relation among acquisition methods, signal processing, and estimators employed. The description spans from current clinical systems for one-and two-dimensional (1-D and 2-D) velocity estimation to the experimental systems for three-dimensional (3-D) estimation and advanced imaging sequences, which can yield thousands of images or volumes per second with fully quantitative flow estimates. Here, spherical and plane wave emissions are employed to insonify the whole region of interest, and full images are reconstructed after each pulse emission for use in velocity estimation.

Lifan Zhao;Xiumei Li;Lu Wang;Guoan Bi; "Autocalibrated Sampling Rate Conversion in the Frequency Domain [Tips & Tricks[Name:_blank]]," vol.34(3), pp.101-106, May 2017. Frequency-domain sampling rate conversion (SRC) can be conveniently implemented by manipulating the discrete Fourier transform (DFT) of the input signal. This method has achieved the advantages of using less computation to obtain more accurate converted output. Conversion errors are mainly produced from the formulation process of the DFT of the output signal. This article presents a sparsity-based scheme to appropriately and automatically calibrate the conversion errors to make further improvement on the conversion accuracy at the cost of more computational complexity. The experimental results demonstrate that the proposed scheme can significantly decrease the mean-square errors (MSEs) and is particularly effective on minimizing the MSEs of phase spectrum.

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Kush R. Varshney; "Signal Processing for Social Good [In the Spotlight[Name:_blank]]," vol.34(3), pp.112-108, May 2017. Communication, speech processing, seismology, and radar are well known applications of signal processing that contribute to the betterment of humanity. But is there a more direct way that signal and information processing can reduce poverty, hunger, inequality, injustice, ill health, and other causes of human suffering? The member states of the United Nations ratified 17 sustainable development goals in 2015, which, if achieved by the targeted year 2030, will end or greatly curtail these problems. Achieving the global goals, however, will require cooperation from all, including the signal processing community. Let me tell you how.

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Stavros Ntalampiras; "Hybrid framework for categorising sounds of Mysticete whales," vol.11(4), pp.349-355, 6 2017. This study addresses a problem belonging to the domain of whale audio processing, more specifically the automatic classification of sounds produced by the Mysticete species. The specific task is quite challenging given the vast repertoire of the involved species, the adverse acoustic conditions and the nearly inexistent prior scientific work. Two feature sets coming from different domains (frequency and wavelet) were designed to tackle the problem. These are modelled by means of a hybrid technique taking advantage of the merits of a generative and a discriminative classifier. The dataset includes five species (Blue, Fin, Bowhead, Southern Right, and Humpback) and it is publicly available at http://www.mobysound.org/. The authors followed a thorough experimental procedure and achieved quite encouraging recognition rates.

Qianru Jiang;Sheng Li;Huang Bai;Rodrigo C. de Lamare;Xiongxiong He; "Gradient-based algorithm for designing sensing matrix considering real mutual coherence for compressed sensing systems," vol.11(4), pp.356-363, 6 2017. This study deals with the issue of designing the sensing matrix for a compressed sensing (CS) system assuming that the dictionary is given. Traditionally, the measurement of small mutual coherence is considered to design the optimal sensing matrix so that the Gram of the equivalent dictionary is as close to the target Gram as possible, where the equivalent dictionary is not normalised. In other words, these algorithms are designed to solve the CS problem using an optimisation stage followed by normalisation. To achieve a global solution, a novel strategy of the sensing matrix design is proposed by using a gradient-based method, in which the measure of real mutual coherence for the equivalent dictionary is considered. According to this approach, a minimised objective function based on alternating minimisation is also developed through searching the target Gram within a set of relaxed equiangular tight frames. Some experiments are done to compare the performance of the newly designed sensing matrix with the existing ones under the condition that the dictionary is fixed. For the simulations of synthetic data and real image, the proposed approach provides better signal reconstruction accuracy.

Nan Pan;Yi Liu;Sixing Wu; "Adaptive matching algorithm for laser detection signals of linear cutting tool marks," vol.11(4), pp.364-371, 6 2017. Since it is very difficult to compare linear cutting tool marks quickly and quantitatively using existing image-processing and three-dimensional scanning methods, an adaptive matching algorithm for laser detection signals of linear cutting marks is proposed. Using locally weighted scatterplot smoothing regression, the proposed algorithm first performs noise reduction on the surface signals of linear cutting tool marks that are detected by a laser displacement sensor. Trends of the thick and consistent features of the signal data are then identified and the feature vectors are quantised via cosine vector curve fitting to individually calculate the spatial distances of the samples. Finally, the most similar samples are matched via batch similarity comparison using a dynamic programming (DP) algorithm. The accuracy and validity of the proposed algorithm are verified by similarity comparison tests of actual cutting marks from a variety of samples.

Nisha Haridas;Elizabeth Elias; "Low-complexity technique to get arbitrary variation in the bandwidth of a digital FIR filter," vol.11(4), pp.372-377, 6 2017. A digital filter is an essential structure in present day electronic devices. There is a variety of applications which demands tunability of digital filters in terms of bandwidth. It is desirable to have simple design process and minimum possible overhead in hardware implementation. A set of very low order subfilters derived from Farrow structure is proposed to function as a sample rate converter on the input signal. A fixed bandwidth low pass filter is placed in between these interpolator structures, resulting in a continuously variable bandwidth filter (CVF). This hardware efficient CVF architecture helps to achieve the tunability without altering the coefficients and the underlying structure. This model obtains a continuous variation in bandwidth employing a single sample rate change factor. Very low complexity and easy tunability of this technique are highly motivating factors to adopt CVF in various real-world applications.

Xiaolong Li;Lingjiang Kong;Guolong Cui;Wei Yi; "Detection and RM correction approach for manoeuvring target with complex motions," vol.11(4), pp.378-386, 6 2017. This study addresses the coherent accumulation problem for detecting a manoeuvring target with complex motions, where range migration (RM) [i.e. range walk (RW) and range curvature (RC)] and Doppler frequency migration (DFM) occur during the coherent integration time. An efficient approach based on generalised keystone transform (GKT), radon transform (RT) and generalised dechirp process (GDP), i.e. GKT-RT-GDP, is presented to eliminate the RM and realise the coherent accumulation. More specifically, the GKT operation is first employed to remove the RC. Then, the RT is applied to estimate the trajectory slope for RW correction and velocity estimation. After that, GDP is introduced to obtain the estimations of target's acceleration and acceleration rate motion. Thereafter, the DFM caused by target's high-order motions can be compensated and then the coherent integration can be realised via Fourier transform. The advantage of the presented algorithm is that it can obtain a good balance between the computation cost and the detection performance, in comparison with the existing coherent integration algorithms.

Xiaofeng Liu;Lin Bo;Honglin Luo; "Directional adaptive kernel distribution and its application," vol.11(4), pp.387-395, 6 2017. The directional adaptive kernel distribution as a new time-frequency analysis method is proposed to analyse the vibration signal of rotor-bearing system. This method extends the adaptive kernel distribution to process the constructed complex-valued signal by defining directional ambiguity function (DAF). Based on the cyclic autocorrelation analysis and the complex-valued signal decomposition, the DAF is proposed, which is the product of the adaptive optimal kernel function and directional cyclic autocorrelation function. The kernel function taken as a two-dimension filter is optimised and used to suppress the cross-terms in the DAF. Then, the directional adaptive kernel distribution is obtained through the positive and inverse Fourier transform on the DAF. The new time-frequency transform is used to analyse the lateral vibration signals of the rotor and the bearing pedestal operating at oil whirling and whipping speeds. The experimental results verified that the proposed method is effective in the characterisation of the fault instantaneous characteristic frequency, rub-impact information, instantaneous planar motion and modulation information etc. in oil-film instability states of rotor-bearing system.

Miloud Bentoumi;Djamel Chikouche;Amar Mezache;Haddi Bakhti; "Wavelet DT method for water leak-detection using a vibration sensor: an experimental analysis," vol.11(4), pp.396-405, 6 2017. In this study, the authors propose and analyse a novel leak-detection method based on the `Haar' continuous wavelet transform (CWT) and a double thresholding, i.e. CWTDT. Inspired by the idea of the binary integration technique in radar target detection, the algorithm analyses the non-stationary vibration signal issued from a water pipeline through which it decides whether or not there exists a leak in the water conveyance. To achieve this, the signal is first divided into several segments. Partial binary decisions within each segment are then obtained through the use of two preselected thresholds. The final binary decision is obtained by means of the `K out of L' fusion rule. In doing this, the hardware leak system prototype is designed and a number of desirable leak positions in the water pipeline are first created to achieve the two best thresholds and `K out of L' fusion rule. For comparison purposes, the performances of the proposed CWTDT method are assessed experimentally against those of the existing fast Fourier transform- and CWT-based methods under real operating conditions.

Jing Lei;Qibin Liu; "Three-dimensional temperature distribution reconstruction using the extreme learning machine," vol.11(4), pp.406-414, 6 2017. The temperature distribution in real-world industrial environments is often in a three-dimensional (3D) space, and developing an efficient algorithm to reconstruct such volume information may be beneficial for increasing the system efficiency, improving the energy saving and reducing the pollutant emission. A new algorithm is put forward for 3D temperature distribution reconstruction (3DTDR) tasks according to the given finite temperature observation data. Owing to the distinct advantages, including fast learning speed, good generalisation ability etc. a new robust regularised extreme learning machine (RRELM) algorithm is developed for the 3DTDR task. Unlike existing tomography-based measurement methods and local point measurement technologies, the proposed algorithm can reconstruct 3D temperature distributions on the basis of the given local temperature observation information. Furthermore, different from available inverse heat transfer problems, the proposed reconstruction method does not solve complicated partial differential equations. Numerical simulation results verify the effectiveness of the RRELM algorithm for 3DTDR problems. Furthermore, the proposed RRELM method has a satisfactory robustness for the noises in the measurement data and can achieve the high-quality reconstruction when the sampling ratio is low. As a result, an effective algorithm is introduced for the 3DTDR problem.

Mustapha Flissi;Khaled Rouabah;Salim Attia;Djamel Chikouche;Thierry Devers; "Consistent BCS modulated signals for GNSS applications," vol.11(4), pp.415-421, 6 2017. In this study, the authors propose reliable sequences of binary coded symbol (BCS) modulation, and their characteristics and performances for Global Navigation Satellite System (GNSS) application are described. A BCS sequence vector is formed by eight variable length sub-chips of alternated + 1 and -1 (or -1 and + 1) values. A judicious choice of the sub-chips lengths of the BCS sequence permitted to propose several BCS sequences that provide high performances in terms of multipath mitigation, resistance to the noise and interferences rejection. An overview of the essential characteristics and the resulting autocorrelation functions (ACFs) and power spectral densities of the proposed BCS sequences were introduced. The latter ACFs have a sharp main peak due to the increase in the number of transitions of the BCS sequences within a chip interval, which corresponds to a larger slope of the discrimination function, and consequently a reduced range of search in the delay locked loop with a minimum calculation load. The theoretical and simulation results indicate that the proposed BCS sequences are more consistent compared to the conventional signals adopted by the GNSS navigation systems.

Zeyu Wang;Ming Li;Yunlong Lu;Hongmeng Chen;Yan Wu; "Efficient TR-TBD algorithm for slow-moving weak multi-targets in heavy clutter environment," vol.11(4), pp.422-428, 6 2017. In this study, the authors present an efficient time-dimension-reduced track-before-detect (TR-TBD) processor for slow-moving weak multi-targets detection in strong clutter environment. In their proposed framework, they elaborate observations from multiple frames (or scans) and resample them in time direction, then distinguish the slow-moving targets from the clutter in the radon parameter domain by exploiting the fact that different velocities of targets have different skewing angles corresponding to their tracks in the range-time (range-pulse) plane. To further enlarge the skewing angles differences between the slow-moving targets and the clutter, TR-TBD is proposed by incorporating the time-dimension reduction operator. This is very helpful to amplify the skewing angle of slow-moving targets, while the improvement is very small for the clutter. Therefore, it is much convenient to figure out the slow-moving weak targets from heavy clutter environment based on their amplified skewing angle differences by setting proper threshold. After detecting the targets, CLEAN-based track recovery method is proposed to eliminate the false tracks and recover the true tracks. Experimental results on real-data demonstrate that the proposed algorithm can detect the closely spaced targets and eliminate the false tracks under low signal-to-noise ratio and signal-to-clutter ratio.

Amiri Parian Mahnaz;Ghofrani Sedigheh; "Mℓ1,2-MUSIC algorithm for DOA estimation of coherent sources," vol.11(4), pp.429-436, 6 2017. Recasting the direction of arrival (DOA) estimation problem into sparse model is a subject of many researches, which has been carried out by different methods. Using the compressive sensing (CS) multiple measurement vector recovery algorithms have improved the resolution of DOA estimation in comparison with conventional methods such as multiple signal classification (MUSIC). In addition, a recently introduced hybrid method using CS and array processing named as ℓ1,2-MUSIC exhibits high resolution as well as capability of resolving coherent sources. However, with recent advances in technology and employing higher order polynomial sources due to non-linear frequency characteristic, the performance of conventional methods is poor when the size of the array is small or the noise level is high. A novel hybrid method is presented based on matching pursuit (MP) signal decomposition and the ℓ1,2-MUSIC algorithm. The new approach named Mℓ1,2-MUSIC takes MP decomposition coefficients of the signal into account and applies the ℓ1,2-MUSIC to achieve DOA estimation. Along with improving the resolvability of closely located non-stationary sources (high ordered polynomial phase) with small-sized sensor array, the main advantage of the proposed method is robustness to source coherency.

Prasad Nizampatnam;Kishore Kumar Tappeta; "Bandwidth extension of narrowband speech using integer wavelet transform," vol.11(4), pp.437-445, 6 2017. Public telephone systems transmit speech across a limited frequency range, about 300-3400 Hz, called narrowband (NB) which results in a significant reduction of quality and intelligibility of speech. This study proposes a fully backward compatible novel method for bandwidth extension (BWE) of NB speech. The method uses integer wavelet transform technique to provide a perceptually better wideband (WB) speech signal. The spectral envelope parameters are extracted from the down sampled frequency shifted version of the high-frequency components of speech signal existing above NB, which are spread by using pseudo-noise codes, and are embedded in the integer wavelet coefficients of NB speech signal. The hearing threshold is calculated in the integer wavelet domain and this threshold is employed as the embedding threshold. The embedded information is extracted at the receiving end to reconstruct the WB speech signal. Theoretical and simulation analyses show that the proposed method is robust to quantisation and channel noises. The comparison category rating listening and log spectral distortion tests clearly show that the reconstructed WB signal gives a much better performance in terms of speech quality when compared with some of the existing speech BWE methods employing data hiding.

Amir Tabatabaei;Mohammad Reza Mosavi;Hadi Shahriar Shahhoseini; "MP Mitigation in Urban Canyons using GPS-combined-GLONASS Weighted Vectorized Receiver," vol.11(4), pp.446-451, 6 2017. Multipath (MP) interference in urban canyons is one of the major sources of the positioning error. Among different methods for MP mitigation, vectorised receiver (VR) is a promising one in which channels can share their information, and as a result the stronger channels can aid the weaker or affected ones to be tracked more accurately. This study proposes a weighted VR (WVR) with the strategy of giving more weights to the observations with lower vulnerability to MP. To increase the number of immune satellites to MP, global positioning system, and global navigation satellite system integration in WVR has been discussed. The performance is also compared with conventional VR. The experimental results show that the proposed method both in a static mode under an exaggerated MP condition and in a drive through an urban canyon could find the position, respectively, with about 10 and 13% improvements.

Hongrui Wang;Zhigang Liu;Yang Song;Xiaobing Lu; "Ensemble EMD-based signal denoising using modified interval thresholding," vol.11(4), pp.452-461, 6 2017. Empirical mode decomposition (EMD) is extensively realised in its potential of non-parametric signal denoising. Ensemble EMD (EEMD) is an improved self-adapting signal decomposition approach that can produce signal components with no frequency aliasing. In this study, the interval thresholding and iteration operation of EMD-based denoising techniques are applied to the EEMD and found not entirely feasible in the EEMD case. A modified interval thresholding is proposed, which can be adjustable for the intrinsic mode functions from EEMD. By taking advantage of the characteristics of EEMD, the internal and external iterations are compared and properly adopted in the EEMD-based denoising strategy. As a result, the EEMD-based denoising methods are proposed by combining the modified interval thresholding and the iterations. The denoising results on synthetic and real-life signals indicate that the presented methods exhibit better performance comparing with EMD-based methods, especially for signals with low signal-to-noise ratio. Based on the time complexities of the proposed methods, the acceptable sampling frequencies of the methods in real-time denoising are given.

Haifeng Li;Guoqi Liu; "Perturbation analysis of signal space fast iterative hard thresholding with redundant dictionaries," vol.11(4), pp.462-468, 6 2017. Practically, sparsity is expressed not in terms of an orthonormal basis but in terms an overcomplete dictionary. There are many practical examples in which a signal of interest is sparse in an overcomplete dictionary. The authors propose a new algorithm signal space fast iterative hard thresholding (SSFIHT) for the recovery of dictionary-sparse signals. Under total perturbations, using D-restricted isometry property (D-RIP), the authors provide the proof of convergence for SSFIHT. Comparing with the error of oracle recovery, it is easy to see that SSFIHT can provide oracle-order recovery performance against total perturbations. Numerical simulations are performed to verify the conclusions.

Yaxing Li;Sangwon Kang; "Deep neural network-based linear predictive parameter estimations for speech enhancement," vol.11(4), pp.469-476, 6 2017. This study presents a speech enhancement technique to improve noise corrupted speech via deep neural network (DNN)-based linear predictive (LP) parameter estimations of speech and noise. With regard to the LP coefficient estimation, an enhanced estimation method using a DNN with multiple layers was proposed. Excitation variances were then estimated via a maximum-likelihood scheme using observed noisy speech and estimated LP coefficients. A time-smoothed Wiener filter was further introduced to improve the enhanced speech quality. Performance was evaluated via log spectral distance, a composite multivariate adaptive regression splines modelling-based measure, and a segmental signal-to-noise ratio. The experimental results revealed that the proposed scheme outperformed competing methods.

Meng Wang;Mao Wang;Shasha Fu;Jianbin Qiu; "New results on <inline-formula><tex-math notation="TeX">${\cal H}_\infty $</tex-math>H<inline-graphic xlink:href="IET-SPR.2016.0469.IM1.gif" /></inline-formula> filter design for sampled-data systems with packet dropouts and transmission delays," vol.11(4), pp.477-485, 6 2017. In this study, the problem of H∞ filtering for sampled-data systems under unreliable communication links is investigated. The phenomena of data packet dropouts and signal transmission delays are addressed in a unified framework. The objective is to design an admissible filter to guarantee the asymptotic stability of the filtering error system and minimise the H∞ disturbance attenuation level. Thanks to the proposed novel Lyapunov-Krasovskii functional together with the improved Wirtinger's inequality and the reciprocally convex approach, novel sufficient linear-matrix-inequality-based conditions are obtained for the existence and design of admissible filters. Finally, two simulation examples are provided to illustrate the efficiency and less conservativeness of the proposed H∞ filter design methods.

Weili Zhou;Qianhua He;Yalou Wang;Yanxiong Li; "Sparse representation-based quasi-clean speech construction for speech quality assessment under complex environments," vol.11(4), pp.486-493, 6 2017. A non-intrusive speech quality assessment method for complex environments was proposed. In the proposed approach, a new sparse representation-based speech reconstruction algorithm was presented to acquire the quasi-clean speech from the noisy degraded signal. Firstly, an over-complete dictionary of the clean speech power spectrum was learned by the K-singular value decomposition algorithm. Then in the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the noise spectrum which was adjusted by a posteriori SNR-weighted factor, and the orthogonal matching pursuit approach was applied to reconstruct the clean speech spectrum from the noisy speech. The quasi-clean speech was considered as the reference to a modified PESQ perceptual model, and the mean opinion score of the noisy degraded speech was achieved via the distortions estimation between the quasi-clean speech and the degraded speech. Experimental results show that the proposed approach obtains a correlation coefficient of 0.925 on NOIZEUS complex environment database, which is 99% similar to the performance of the intrusive standard ITU-T PESQ, and 7.1% outperforms non-intrusive standard ITU-T P.563.

IEEE Transactions on Geoscience and Remote Sensing - new TOC (2017 June 19) [Website]

* "Front Cover," vol.55(6), pp.C1-C1, June 2017.* Presents the front cover for this issue of the publication.

* "IEEE Transactions on Geoscience and Remote Sensing publication information," vol.55(6), pp.C2-C2, June 2017.* Presents a listing of the editorial board, board of governors, current staff, committee members, and society editors for this issue of the publication.

* "Table of contents," vol.55(6), pp.3069-3636, June 2017.* Presents the table of contents for this issue of the publication.

Zengmao Wang;Bo Du;Lefei Zhang;Liangpei Zhang;Xiuping Jia; "A Novel Semisupervised Active-Learning Algorithm for Hyperspectral Image Classification," vol.55(6), pp.3071-3083, June 2017. Less training samples are a challenging problem in hyperspectral image classification. Active learning and semisupervised learning are two promising techniques to address the problem. Active learning solves the problem by improving the quality of the training samples, while semisupervised learning solves the problem by increasing the quantity of the training samples. However, they pay too much attention to the discriminative information in the unlabeled data, leading to information bias to train supervised models, and much more effort to label samples. Therefore, a method to discover representativeness and discriminativeness by semisupervised active learning is proposed. It takes advantages of both active learning and semisupervised learning. The representativeness and discriminativeness are discovered with a labeling process based on a supervised clustering technique and classification results. Specifically, the supervised clustering results can discover important structural information in the unlabeled data, and the classification results are also highly confidential in the active-learning process. With these clustering results and classification results, we can assign pseudolabels to the unlabeled data. Meanwhile, the unlabeled samples that cannot be assigned with pseudolabels with high confidence at each iteration are regarded as candidates in active learning. The methodology is validated on four hyperspectral data sets. Significant improvements in classification accuracy are achieved by the proposed method with respect to the state-of-the-art methods.

Steven Hancock;Rachel Gaulton;F. Mark Danson; "Angular Reflectance of Leaves With a Dual-Wavelength Terrestrial Lidar and Its Implications for Leaf-Bark Separation and Leaf Moisture Estimation," vol.55(6), pp.3084-3090, June 2017. A new generation of multiwavelength lidars offers the potential to measure the structure and biochemistry of vegetation simultaneously, using range resolved spectral indices to overcome the confounding effects in passive optical measurements. However, the reflectance of leaves depends on the angle of incidence, and if this dependence varies between wavelengths, the resulting spectral indices will also vary with the angle of incidence, complicating their use in separating structural and biochemical effects in vegetation canopies. The Salford Advanced Laser Canopy Analyser (SALCA) dual-wavelength terrestrial laser scanner was used to measure the angular dependence of reflectance for a range of leaves at the wavelengths used by the new generation of multiwavelength lidars, 1063 and 1545 nm, as used by SALCA, DWEL, and the Optech Titan. The influence of the angle of incidence on the normalized difference index (NDI) of these wavelengths was also assessed. The reflectance at both wavelengths depended on the angle of incidence and could be well modelled as a cosine. The change in the NDI with the leaf angle of incidence was small compared with the observed difference in the NDI between fresh and dry leaves and between leaf and bark. Therefore, it is concluded that angular effects will not significantly impact leaf moisture retrievals or prevent leaf/bark separation for the wavelengths used in the new generation of 1063- and 1545-nm multiwavelength lidars.

Hongjie He;Yudong Lin;Fan Chen;Heng-Ming Tai;Zhongke Yin; "Inshore Ship Detection in Remote Sensing Images via Weighted Pose Voting," vol.55(6), pp.3091-3107, June 2017. Inshore ship detection from high-resolution satellite images is a useful yet challenging task in remote surveillance and military reconnaissance. It is difficult to detect the inshore ships with high precision because various interferences are present in the harbor scene. An inshore ship detection method based on the weighted voting and rotation-scale-invariant pose is proposed to improve the detection performance. The proposed method defines the rotation angle pose and the scaling factor of the detected ship to detect the ship with different directions and different sizes. For each pixel on the ship template, the possible poses of a detection window are estimated according to all possible pose-related pixels. To improve robustness to the shape-similar distractor and various interferences, the score of the detection window is obtained by designing a pose weighted voting method. Moreover, the values of some parameters such as similarity threshold and the weight of “V” are investigated. The experimental results on actual satellite images demonstrate that the proposed method is invariant to rotation and scale and robust in the inshore ship detection. In addition, better detection performance is observed in comparison with the existing inshore ship detection algorithms in terms of precision rate and recall rate. The target pose of the detected ship can also be obtained as a byproduct of the ship detection.

Abdollah Kavousi-Fard;Wencong Su; "A Combined Prognostic Model Based on Machine Learning for Tidal Current Prediction," vol.55(6), pp.3108-3114, June 2017. This paper proposes a univariate prognostic approach based on wavelet transform and support vector regression (SVR) to predict the tidal current speed and direction with high accuracy. The proposed model decomposes the tidal current data into some subharmonic components. The details and approximation components are later fed to several SVR models to attend the prediction process. In order to increase the robustness of the model, the idea of combined prediction is used to model each subharmonic signal by several SVRs. The median operator is further used to determine the aggregated forecast tidal current data. Due to the high reliance of SVR model on the kernel function and hyperplane parameters, a new optimization method based on the bat algorithm is used to train the SVR model. The final forecast tidal current data are constructed using an aggregation operator in the output of the SVRs. The accuracy and satisfying performance of the proposed model are examined on the practical tidal data collected from the Bay of Fundy, NS, Canada. The experimental results reveal the high capability and robustness of the proposed hybrid model for the tidal current prediction.

Deliang Xiang;Yifang Ban;Wei Wang;Yi Su; "Adaptive Superpixel Generation for Polarimetric SAR Images With Local Iterative Clustering and SIRV Model," vol.55(6), pp.3115-3131, June 2017. Simple linear iterative clustering (SLIC) algorithm was proposed for superpixel generation on optical images and showed promising performance. Several studies have been proposed to modify SLIC to make it applicable for polarimetric synthetic aperture radar (PolSAR) images, where the Wishart distance is adopted as the similarity measure. However, the superpixel segmentation results of these methods were not satisfactory in heterogeneous urban areas. Further, it is difficult to determine the tradeoff factor which controls the relative weight between polarimetric similarity and spatial proximity. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel generation method is proposed to overcome these limitations. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel generation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed superpixel generation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.

Peng Liu;Ya-Qiu Jin; "A Study of Ship Rotation Effects on SAR Image," vol.55(6), pp.3132-3144, June 2017. Imaging distortions induced by each particular rotation of a moving ship are quantitatively investigated through numerical simulations. Two sets of dynamics are considered in the model, namely a ship with pitch, yaw, and roll rotations, and the time evolutions of its wake. To construct high-resolution synthetic aperture radar (SAR) images in spotlight mode, the time-varying scattering from the electrically very large scene is computed by a parallel quasi-stationary algorithm, in which the physical optics (PO) method is used to compute the scattering from the ship, and a PO phase correction of the two-scale model is used to take account of the Doppler effects caused by the wake. Reasonable agreement is obtained when comparisons are made between the simulated and real SAR images. A range of imaging distortions are observed and analyzed, such as the displacement, rotation, stretching/compressing, and broaden/narrow of the ship image. A systematic analysis shows that these distortions can be characterized by four main types of transformations, namely translation, rotation, scaling, and shearing. This paper presents quantitative insights into the data interpretation and signature classification of ship on SAR image.

Alfio V. Parisi;Nathan Downs;Joanna Turner;Rachel King; "Comparison of GOME-2 UVA Satellite Data to Ground-Based Spectroradiometer Measurements at a Subtropical Site," vol.55(6), pp.3145-3149, June 2017. The ultraviolet A (UVA) (315-400 nm) daily exposures and maximum daily irradiances from the Global Ozone Monitoring Experiment (GOME)-2 satellite have been compared over three years to the corresponding data from a groundbased spectroradiometer for a subtropical Southern Hemisphere site. This is one of the first such comparisons for the GOME-2 UVA waveband in the Southern Hemisphere. For the UVA daily exposures and the maximum daily irradiances, the comparisons were undertaken for all sky conditions and for cloud-free conditions. Under cloud-free conditions the R2 of the fit regression line for the comparisons was 0.93 for the exposures and the irradiances. The influence of cloud reduced the R2 values to 0.86 and 0.70 for the daily exposures and maximum irradiances, respectively. The relative root-mean-square error (rRMSE), mean absolute bias error (MABE), and mean bias error (MBE) for the maximum daily UVA irradiances on the cloud-free days were 0.08, 6.59 ± 7.32%, and -1.04 ± 9.83%, respectively. Similarly, for the daily UVA exposures on the cloud-free days, the rRMSE, MABE, and MBE were 0.10, 5.19 ± 6.42%, and -0.79 ± 8.24%, respectively. For the all-sky conditions, the corresponding values were 0.20, 15.23 ± 14.90%, and -0.79 ± 8.24% for the maximum daily irradiances and 0.19, 14.17 ± 14.56%, and -4.63 ± 20.60% for the daily exposures. In all studies of the influence of UVA on human health, this can complement ground-based measurements that provide the higher temporal and spatial resolution available only at a limited number of surface monitoring sites.

Yunlong Li;Peter Hoogeboom;Herman W. J. Russchenberg; "A Novel Radar-Based Visibility Estimator," vol.55(6), pp.3150-3168, June 2017. Remote visibility (Vis) estimation by radar is of interest to aviation, road traffic, and other fields. Millimeter-wave radars are suitable candidates because of such advantages as high spatial resolution and sensitivity to small droplets in reflection and attenuation. To investigate remote Vis estimation and to develop physics-based models and algorithms, a 35 GHz cloud radar at the Cabauw Experimental Site for Atmospheric Research (CESAR) in the western part of the Netherlands has acquired data during fog periods in “fog mode.” The advantage in using millimeter-waves for remote sensing of fog is that they interact strong enough with fog for sensing it, but not too strong so that they can penetrate the fog allowing to sense fog top. Simultaneously, fog drop size distribution (DSD) and Vis are continuously automatically measured by the in situ optical sensors at CESAR. Radar reflectivity (Z) and Vis can be linked theoretically since they are related to the sixth and the second moments of an assumed Gamma-shaped DSD. However, in reality the fog DSD is not always Gamma-shaped, leading to errors in the Vis estimation. A further development of the Vis-Z model includes the attenuation factor (La), which is proportional to the liquid water content at a given radar wavelength. This improves the estimated accuracy of Vis, in the theoretical moments-based model. Finally, we were able to arrive at a higher accuracy by introducing an empirical exponential model, estimating Vis from Z and La. A test based on DSD data sets for various fog types in the literature showed robust performance of the Vis-Z-La model for large variations in DSD. The Vis-Z-La model is also validated with the actual fog data sets that were collected by the in situ and remote sensing instruments synergy at CESAR.

Alexander G. Voronovich;Valery U. Zavorotny; "Measurement of Ocean Wave Directional Spectra Using Airborne HF/VHF Synthetic Aperture Radar: A Theoretical Evaluation," vol.55(6), pp.3169-3176, June 2017. Currently, images obtained with microwave synthetic aperture radars (SARs) are widely used for measuring directionality of ocean waves on a global and continuous scale. However, with all the advantages of the microwave SAR systems, the effectiveness of wave spectrum retrieval from SAR images is still debated. In this paper, we demonstrate how the directional spectrum of relatively long sea waves can be measured using aperture synthesis method in conjunction with relatively low radio frequencies (HF and VHF bands). This approach has advantages over HF ground-based radars used for ocean wave studies in coastal zones. In this paper, we theoretically evaluate this technique for the system mounted on aircraft. A favorable combination of the parameters for both ocean surface waves and HF electromagnetic waves allows an accurate analytical description of scattering from the sea surface based on the first approximation of the small perturbation method. In this case, scattered electromagnetic field becomes a linear functional of sea surface wave elevations. An analysis of the signal processing pertaining to this situation is presented, and a practical example is discussed. The proposed approach can be used even in the case when the radar platform is moving nonsteadily and/or along the curvilinear trajectory.

Sean Bryan;Amanda Clarke;Loÿc Vanderkluysen;Christopher Groppi;Scott Paine;Daniel W. Bliss;James Aberle;Philip Mauskopf; "Measuring Water Vapor and Ash in Volcanic Eruptions With a Millimeter-Wave Radar/Imager," vol.55(6), pp.3177-3185, June 2017. Millimeter-wave remote sensing technology can significantly improve measurements of volcanic eruptions, yielding new insights into eruption processes and improving forecasts of drifting volcanic ash for aviation safety. Radiometers can measure water vapor density and temperature inside eruption clouds, improving on existing measurements with infrared cameras that are limited to measuring the outer cloud surface. Millimeter-wave radar can measure the 3-D mass distribution of volcanic ash inside eruption plumes and their nearby drifting ash clouds. Millimeter wavelengths are better matched to typical ash particle sizes, offering better sensitivity than longer wavelength existing weather radar measurements, as well as the unique ability to directly measure ash particle size in situ. Here we present sensitivity calculations in the context of developing the water and ash millimeter-wave spectrometer (WAMS) instrument. WAMS, a radar/radiometer system designed to use off-the-shelf components, would be able to measure water vapor and ash throughout an entire eruption cloud, a unique capability.

Jeffrey D. Ouellette;Joel T. Johnson;Anna Balenzano;Francesco Mattia;Giuseppe Satalino;Seung-Bum Kim;R. Scott Dunbar;Andreas Colliander;Michael H. Cosh;Todd G. Caldwell;Jeffrey P. Walker;Aaron A. Berg; "A Time-Series Approach to Estimating Soil Moisture From Vegetated Surfaces Using L-Band Radar Backscatter," vol.55(6), pp.3186-3193, June 2017. Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection of in situ networks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3 over a variety land cover types.

Chunfeng Ma;Xin Li;Claudia Notarnicola;Shuguo Wang;Weizhen Wang; "Uncertainty Quantification of Soil Moisture Estimations Based on a Bayesian Probabilistic Inversion," vol.55(6), pp.3194-3207, June 2017. Soil moisture (SM) inversions based on active microwave remote sensing have shown promising progress but do not easily meet expected application requirements because a number of inversion algorithms can only produce point estimates of SM and cannot quantify the uncertainty of SM inversions. Although previous studies have reported Bayesian maximum posterior estimations that are capable of retrieving SM within a probabilistic framework, they have primarily focused on the optimal estimators of SM and have typically ignored the uncertainty of SM inversions. This paper presents an SM probabilistic inversion (PI) algorithm based on Bayes' theorem and the Markov Chain Monte Carlo technique and capable of revealing the uncertainty of SM inversions and obtaining highly accurate SM estimates via maximum likelihood estimations (MLEs). The algorithm is implemented based on the advanced integral equation model, water cloud model simulations, and dual-polarized TerraSAR-X observations. The ground SM and vegetation water content (VWC) measurements from the Heihe watershed allied telemetry experimental research experiments are applied for validation. The results show that: 1) uncertainties in SM inversions, defined with respect to the measures of dispersion of SM posterior probability distribution, are approximately 0.1-0.12 m3/m3 and 2) an acceptable inversion accuracy is obtained via MLEs, which present an SM Root Mean Square Error (RMSE) of 0.045 and 0.047 m3/m3 for bare and vegetated soils, respectively, and a VWC RMSE of 0.45 kg/m2. The presented PI can quantify the uncertainty in SM inversions; therefore, it should be useful for improving active microwave remote sensing estimations of SM.

Rui Zhao;Bo Du;Liangpei Zhang; "Hyperspectral Anomaly Detection via a Sparsity Score Estimation Framework," vol.55(6), pp.3208-3222, June 2017. Anomaly detection has become an important topic in hyperspectral imagery (HSI) analysis over the last 20 years. HSIs usually possess complexly cluttered spectral signals due to the complicated conditions of the land-cover distribution. This in turn makes it difficult to obtain an accurate background estimation to distinguish the anomaly targets. The sparse learning technique provides a way to obtain an implicit background representation with the learned dictionary and corresponding sparse codes. In this paper, we explore the background/anomaly information content for each atom of the learned dictionary, from an analysis based on the frequency of the dictionary atoms for HSI reconstruction. From this perspective, we propose a novel sparsity score estimation framework for hyperspectral anomaly detection. First, an overcomplete dictionary and the corresponding sparse code matrix are obtained from the HSI. The frequency of each dictionary atom for reconstruction, which is also called the atom usage probability, is then estimated from the sparse code matrix. Finally, the estimated frequencies are transformed to the sparsity score for each pixel, which can be seen as the degree of “anomalousness.” In the proposed detection framework, two strategies are proposed to enhance the diversity between the background and anomaly information in the learned dictionary: 1) dictionary-based background feature transformation and 2) dictionary iterative reweighting. A series of real-world HSI data sets is utilized to evaluate the performance of the proposed framework. The experimental results show that the proposed framework achieves a superior performance compared to some of the state-of-the-art anomaly detection methods.

Yu Liu;Kun-Shan Chen;Yuan Liu;Jiangyuan Zeng;Peng Xu;Zhao-Liang Li; "On Angular Features of Radar Bistatic Scattering From Rough Surface," vol.55(6), pp.3223-3235, June 2017. In this paper, an attempt is made to investigate the angular signatures of bistatic scattering, in the azimuthal direction, from rough surfaces, with the aim of deepening our understanding of the bistatic scattering behaviors and exploring its potential applications. Three distinct angular features, dip angle, scattering strength, and angular width, as a function of the surface roughness and dielectric constant, are identified. Brewster's scattering, and its role in angular behavior, is examined at limited extent. Results reveal that the angular features strongly correlate with the surface parameters and scattering geometry. For small scattering angle, dip angle and width are independent of surface roughness. Comparatively, for larger incident and scattering angles, beyond 50°, the dip angle and scattering strength are sensitive, simultaneously, to rms height and dielectric constant, while the dip width only responses to rms height. Dips, induced by Brewster's scattering effect, not only shift in the polar direction, but also in the azimuthal direction, and are strongly dependent on surface parameters and bistatic geometry. Increasing the surface roughness or, equivalently, the incident angle tends to promote the disappearance of dips. The main contributions of this paper can be summarized as follows: 1) quantitative description of dip features, including angle, scattering strength, and angular width; 2) comprehensive characterization of the dip features and their dependence on surface parameters and bistatic geometries; and 3) limited investigation of the behavior of Brewster's scattering-induced dip.

Yongzheng Ren;Xiao-Ming Li;GuoPing Gao;Thomas Edmund Busche; "Derivation of Sea Surface Tidal Current From Spaceborne SAR Constellation Data," vol.55(6), pp.3236-3247, June 2017. In this paper, we demonstrate an application of spaceborne synthetic aperture radar (SAR) constellation data to derive sea surface tidal current at high spatial resolution. The maximum cross correlation (MCC) method, which has been widely applied to optical remote sensing data to derive sea surface velocities, is applied to X-band SAR data from TerraSAR-X (TSX) and TanDEM-X (TDX), which were operating in pursuit monostatic mode. Because of the short temporal interval of TSX and TDX's pursuit acquisitions, temporal de-correlation is minimized to derive tidal current fields that exhibit significant temporal and spatial variations. In addition, we combined data from TDX and another X-band SAR, COSMO-SkyMed, to obtain a virtual SAR constellation data pair, which was also analyzed using the MCC method to derive the tidal current field. Case studies of Hangzhou Bay in the East China Sea and Amrum Island in the North Sea are presented. The SAR-derived tidal current fields were compared to the results of numerical model simulations, high-frequency (HF) radar measurements and in situ measurements. MCC coefficients that are greater than 0.8 are an appropriate threshold for the further derivation of tidal currents. Comparisons to finite volume community ocean model, HF radar and general estuarine transport model results yield root-mean-square errors of 0.13, 0.06, and 0.05 m/s, respectively. Measurements from three field stations were also compared to the MCC SAR retrievals, yielding differences of 0.3, 0.07, and -0.09 m/s.

Abigael Taylor;Hélène M. Oriot;Laurent Savy;Franck Daout;Philippe Forster; "Moving Targets Detection Capacities Improvement in Multichannel SAR Framework," vol.55(6), pp.3248-3260, June 2017. When using multichannel synthetic aperture radar (MSAR) to perform moving target detection, the coherence between the received signals is closely linked to the detection capacities of the system. In airborne MSAR context, because of the platform movement, the antenna patterns, as well as the phase shifts between the channels, evolve in different ways during the integration time. In this paper, these dissimilarities are shown to cause a degradation of coherence, and hence of detection capacities. A method to enhance the coherence is then developed. This method is based on the estimation of the steering vector. It is applied on two sets of real data, and the obtained results validate the method.

Ji Wu;Cheng Zhang;Hao Liu;Jingye Yan; "Performance Analysis of Circular Antenna Array for Microwave Interferometric Radiometers," vol.55(6), pp.3261-3271, June 2017. The microwave interferometric radiometer (MIR) is a passive sensor using synthetic aperture technique for microwave remote sensing. It uses a thinned antenna array to replace the traditional large real aperture antenna, so as to reduce the antenna size, weight, and manufacturing difficulty, and further increase the spatial resolution of the instrument. The antenna array configuration is the first concern for the MIR application. Various array configurations have been proposed. Among them the arrays with hexagonal sampling grid such as the Y-shaped and hexagonal arrays have drawn more attention because of the higher sampling efficiency. However, they do not have the best performance in radiometric sensitivity and radio frequency interference (RFI) mitigation. This paper presents the detailed analysis of the circular array performance and compares it with the Y-shaped and hexagonal arrays. Based on the early developed theory about the radiometric resolution, two new factors named as sampling sensitivity factor and uncertainty factor are first introduced and defined in this paper, which can be used to quantitatively characterize the array performance. The main beam efficiency of the MIR is also redefined to prevent it being uncomfortably greater than unity but still keep a physical significance. Numerical simulations with analysis are finally implemented to verify the theoretical results and also to demonstrate the superiority of the circular array in terms of radiometric sensitivity and RFI mitigation.

Yuki Itoh;Siwei Feng;Marco F. Duarte;Mario Parente; "Semisupervised Endmember Identification in Nonlinear Spectral Mixtures via Semantic Representation," vol.55(6), pp.3272-3286, June 2017. This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semisupervised fashion, assuming the availability of a spectral reference library. Existing semisupervised unmixing algorithms select members from an endmember library that are present at each of the pixels; most such methods assume a linear mixing model. However, those methods will fail in the presence of nonlinear mixing among the observed spectra. To address this issue, we develop an endmember selection method using a recently proposed semantic spectral representation obtained via nonhomogeneous hidden Markov chain model for a wavelet transform of the spectra. The semantic representation can encode spectrally discriminative features for any observed spectrum, and therefore, our proposed method can perform endmember selection without any assumption on the mixing model. The experimental results show that in the presence of sufficiently nonlinear mixing, our proposed method outperforms dictionary-based sparse unmixing approaches based on linear models.

Michelangelo Villano;Gerhard Krieger;Alberto Moreira; "New Insights Into Ambiguities in Quad-Pol SAR," vol.55(6), pp.3287-3308, June 2017. Quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) is a well-established technique which has numerous applications in remote sensing. However, spaceborne quad-pol SAR systems are often constrained by severe range and azimuth ambiguities. A deep understanding of the nature of ambiguities in conventional, hybrid, and ±π/4 quad-pol SAR allows a correct evaluation of the ambiguity-to-signal ratio and the design of systems optimized for ambiguity suppression. In that respect azimuth phase coding and merging data available from both cross-polarized channels play an important role. The results of this paper are relevant not only for the design of future spaceborne quad-pol SAR systems, including staggered SAR ones, but also for the optimal exploitation of quad-pol SAR systems currently in operation.

Guang-Cai Sun;Yuan Wu;Jun Yang;Mengdao Xing;Zheng Bao; "Full-Aperture Focusing of Very High Resolution Spaceborne-Squinted Sliding Spotlight SAR Data," vol.55(6), pp.3309-3321, June 2017. In very high resolution spaceborne-squinted sliding spotlight synthetic aperture radar, the traditional imaging algorithms based on the equivalent squint range model (ESRM) cannot be applied, because the ESRM model is inaccurate in this case. For this problem, this paper proposes a squint equivalent acceleration range model to precisely take into account the spaceborne-squinted curved orbit. Then a full-aperture squint-imaging algorithm is proposed based on this new range model, which can handle the azimuth variation of the equivalent velocity and the range variation of the 2-D frequency spectrum. The results of the simulation validate the effectiveness of new range model and imaging algorithm.

Guangliang Cheng;Ying Wang;Shibiao Xu;Hongzhen Wang;Shiming Xiang;Chunhong Pan; "Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network," vol.55(6), pp.3322-3337, June 2017. Accurate road detection and centerline extraction from very high resolution (VHR) remote sensing imagery are of central importance in a wide range of applications. Due to the complex backgrounds and occlusions of trees and cars, most road detection methods bring in the heterogeneous segments; besides for the centerline extraction task, most current approaches fail to extract a wonderful centerline network that appears smooth, complete, as well as single-pixel width. To address the above-mentioned complex issues, we propose a novel deep model, i.e., a cascaded end-to-end convolutional neural network (CasNet), to simultaneously cope with the road detection and centerline extraction tasks. Specifically, CasNet consists of two networks. One aims at the road detection task, whose strong representation ability is well able to tackle the complex backgrounds and occlusions of trees and cars. The other is cascaded to the former one, making full use of the feature maps produced formerly, to obtain the good centerline extraction. Finally, a thinning algorithm is proposed to obtain smooth, complete, and single-pixel width road centerline network. Extensive experiments demonstrate that CasNet outperforms the state-of-the-art methods greatly in learning quality and learning speed. That is, CasNet exceeds the comparing methods by a large margin in quantitative performance, and it is nearly 25 times faster than the comparing methods. Moreover, as another contribution, a large and challenging road centerline data set for the VHR remote sensing image will be publicly available for further studies.

Xueyang Duan;Mahta Moghaddam; "Full-Wave Electromagnetic Scattering From Rough Surfaces With Buried Inhomogeneities," vol.55(6), pp.3338-3353, June 2017. We develop a methodology for modeling coherent electromagnetic scattering from rough surfaces with buried inhomogeneities in three dimensions. The inhomogeneities considered in this paper include random spherical media, random cylindrical media, and root-like cylindrical clusters. They are used to simulate rocks, ice particles, and vegetation roots buried beneath the ground surface that can be seen by the low-frequency radars in Earth remote sensing applications. The approach we develop first calculates volumetric scattering from the media using coherent approaches, including both the conventional recursive transition matrix (T-matrix) method as well as a new generalized iterative extended boundary condition method we developed for tilted finite cylinders, and then transforms the T-matrix to the scattering matrix, which is then used to form the full scattered field of layered structures with rough surface and subsurfaces. We validate the methodology by comparing with other numerical solutions for special cases, and show sensitivity results for scattering from rough surface with buried random spherical media and random cylindrical media of different densities. We also construct a basic root model and calculate the scattering cross sections from single and multiple root clusters with or without a subsurface interface underneath. With the approach developed in this paper, we are able to study the sensitivity of radar signals to subsurface scatterers. For example, our simulations show that, depending on their density and water content, buried roots could enhance the backscatter from a single rough surface by as much as 5 dB in co-pol components, and substantially more in cross-pol components. The results of this model are expected to enable more accurate geophysical retrievals of soil moisture as well as soil organic content.

Naveed Akhtar;Ajmal Mian; "RCMF: Robust Constrained Matrix Factorization for Hyperspectral Unmixing," vol.55(6), pp.3354-3366, June 2017. We propose a constrained matrix factorization approach for linear unmixing of hyperspectral data. Our approach factorizes a hyperspectral cube into its constituent endmembers and their fractional abundances such that the endmembers are sparse nonnegative linear combinations of the observed spectra themselves. The association between the extracted endmembers and the observed spectra is explicitly noted for physical interpretability. To ensure reliable unmixing, we make the matrix factorization procedure robust to outliers in the observed spectra. Our approach simultaneously computes the endmembers and their abundances in an efficient and unsupervised manner. The extracted endmembers are nonnegative quantities, whereas their abundances additionally follow the sum-to-one constraint. We thoroughly evaluate our approach using synthetic data with white and correlated noise as well as real hyperspectral data. Experimental results establish the effectiveness of our approach.

Michael Kwok-Po Ng;Qiangqiang Yuan;Li Yan;Jing Sun; "An Adaptive Weighted Tensor Completion Method for the Recovery of Remote Sensing Images With Missing Data," vol.55(6), pp.3367-3381, June 2017. Missing information, such as dead pixel values and cloud effects, is very common image quality degradation problems in remote sensing. Missing information can reduce the accuracy of the subsequent image processing, in applications such as classification, unmixing, and target detection, and even the quantitative retrieval process. The main aim of this paper is to study an adaptive weighted tensor completion (AWTC) method for the recovery of remote sensing images with missing data. Our idea is to collectively make use of the spatial, spectral, and temporal information to build a new weighted tensor low-rank regularization model for recovering the missing data. In the model, the weights are determined adaptively by considering the contribution of the spatial, spectral, and temporal information in each dimension. Experimental results based on both simulated and real data sets are presented to verify that the proposed method can recover missing data, and its performance is found to be better than the other tested methods. In the simulated experiments, the peak signal-to-noise ratio is improved by more than 3 dB, compared with the original tensor completion model. In the real data experiments, the proposed AWTC model can better recover the dead line problem in Aqua Moderate Resolution Imaging Spectroradiometer band 6 and the scan-line corrector-off problem in enhanced thematic mapper plus images, with the smallest spectral distortion.

Sergei Rudenko;Karl-Hans Neumayer;Denise Dettmering;Saskia Esselborn;Tilo Schöne;Jean-Claude Raimondo; "Improvements in Precise Orbits of Altimetry Satellites and Their Impact on Mean Sea Level Monitoring," vol.55(6), pp.3382-3395, June 2017. New, precise, consistent orbits (VER11) of altimetry satellites ERS-1, ERS-2, TOPEX/Poseidon, Envisat, Jason-1, and Jason-2 have been recently derived at the GFZ German Research Centre for Geosciences in the extended ITRF2008 terrestrial reference frame using improved models and covering the time span 1991-2015. These orbits show improved quality, as compared with GFZ previous (VER6) orbits derived in 2013. Improved macromodels reduce root mean square (RMS) fits of satellite laser ranging (SLR) observations by 2.6%, 6.9%, and 7% for TOPEX/Poseidon, Jason-1, and Jason-2, respectively. Applying Vienna Mapping Functions 1 instead of Hopfield model for tropospheric correction of Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) observations reduces RMS fits of SLR observations by 2%-2.4% and those of DORIS observations by 2.6% for Envisat and Jason satellites. Using satellite true attitude instead of models improves Jason-1 SLR RMS fits by 41% from July 2012 until July 2013. The VER11 orbits indicate the mean values of the SLR RMS fits between 1.2 and 2.1 cm for the different missions. The internal orbit consistency in the radial direction is between 0.5 and 1.9 cm. The global mean sea level trend for the period 1993-2014 from TOPEX, Jason-1, and Jason-2 is 2.8 and 3.0 mm/year using GFZ VER6 and VER11 orbits, respectively. Regionally, the decadal trends from GFZ VER11 and external orbits vary in the order of 1 mm/year.

Bin Zou;Da Lu;Lamei Zhang;Wooil M. Moon; "Independent and Commutable Target Decomposition of PolSAR Data Using a Mapping From SU(4) to SO(6)," vol.55(6), pp.3396-3407, June 2017. Polarimetric target decomposition is the most commonly used method of extracting information from polarimetric synthetic aperture radar (SAR) images. Coherent target decomposition methods are usually suitable for high-resolution images. Recently, Paladini reviewed coherent target decomposition methods and proposed a new approach, lossless and sufficient target decomposition (LSTD), using the special unitary matrix SU(4). However, this method suffers from parameter dependence and commutation problems that could introduce errors in parameter estimation such as an erroneous odd-even bounce ratio. In order to overcome these problems, a new model to decompose the circular polarization scattering vector is proposed. In this paper, the model applies a mapping from SU(4) to SU(6) to simplify the target representation while meaningful parameters, which are independent, can be extracted. Fully polarimetric L-band UAVSAR data are used to validate the proposed method. The most important odd-even bounce ratio parameter is used to compare the estimation accuracy between the proposed method and LSTD. Results show that the proposed method can extract parameters more accurately.

Lei Ran;Zheng Liu;Lei Zhang;Tao Li;Rong Xie; "An Autofocus Algorithm for Estimating Residual Trajectory Deviations in Synthetic Aperture Radar," vol.55(6), pp.3408-3425, June 2017. Due to the accuracy limitation of the navigation system, deviations between the real trajectory and the measured one appear inevitably in airborne synthetic aperture radar (SAR), which degrades the image quality dramatically. To improve the focusing performance, these trajectory deviations should be well estimated and compensated. In this paper, a data-based autofocus approach is proposed to correct the residual 3-D trajectory deviations. This new approach mainly contains two processing stages. The first stage is the local phase error estimation procedure involving small images autofocusing. A gradient function considering smoothness regularization is developed to efficiently achieve the sharpness-maximizing local phase error functions. In the second stage, the local phase error functions are combined to retrieve the residual 3-D trajectory deviations by a proposed weighted total least square method. This approach has been applied on highly squinted and large-swath airborne SAR raw data, respectively. Both real data experiments generate well-focused SAR images by the estimated trajectory parameters, and thus, validate the effectiveness of the proposed autofocus approach.

Hui Bi;Bingchen Zhang;Xiao Xiang Zhu;Wen Hong;Jinping Sun;Yirong Wu; "$L_{1}$ -Regularization-Based SAR Imaging and CFAR Detection via Complex Approximated Message Passing," vol.55(6), pp.3426-3440, June 2017. Synthetic aperture radar (SAR) is a widely used active high-resolution microwave imaging technique that has alltime and all-weather reconnaissance ability. Compared with traditionally matched filtering (MF)-based methods, Lq(0 ≤ q ≤ 1) regularization technique can efficiently improve SAR imaging performance e.g., suppressing sidelobes and clutter. However, conventional Lq-regularization-based SAR imaging approach requires transferring the 2-D echo data into a vector and reconstructing the scene via 2-D matrix operations. This leads to significantly more computational complexity compared with MF, and makes it very difficult to apply in high-resolution and wide-swath imaging. Typical Lq regularization recovery algorithms, e.g., iterative thresholding algorithm, can improve imaging performance of bright targets, but not preserve the image background distribution well. Thus, image background statistical-property-based applications, such as constant false alarm rate (CFAR) detection, cannot be applied to regularization recovered SAR images. On the other hand, complex approximated message passing (CAMP), an iterative recovery algorithm for L1 regularization reconstruction, can achieve not only the sparse estimation of the original signal as typical regularization recovery algorithms but also a nonsparse solution simultaneously. In this paper, two novel CAMP-based SAR imaging algorithms are proposed for raw data and complex radar image data, respectively, along with CFAR detection via the CAMP recovered nonsparse result. The proposed method for raw data can not only improve SAR image performance as conventional L1 regularization technique but also reduce the computational cost efficiently. While only when we have MF recovered SAR complex image rather than raw data, the proposed method for complex image data can achieve a similar reconstructed image quality as the regularization-ba- ed SAR imaging approach using the full raw data. The most important contribution of this paper is that the proposed CAMP-based methods make CFAR detection based on the regularization reconstruction SAR image possible using their nonsparse scene estimations, which has a similar background statistical distribution as the MF recovered images. The experimental results validated the effectiveness of the proposed methods and the feasibility of the recovered nonsparse images being used for CFAR detection.

Z. S. Haddad;O. O. Sy;S. Hristova-Veleva;G. L. Stephens; "Derived Observations From Frequently Sampled Microwave Measurements of Precipitation—Part I: Relations to Atmospheric Thermodynamics," vol.55(6), pp.3441-3453, June 2017. This is the first of two papers that quantify the high added value of frequent 3-D radar observations of the atmosphere to capture the dynamics of weather systems. Recent advances in small-satellite and radar technologies, such as the “Radar in Cubesat” developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between such observations to the underlying cloud variables and processes, and to quantify the sensitivity to the different physical and instrument parameters. In this first part, a robust algorithm is proposed to estimate the horizontal advection from successive radar reflectivity measurements, and use it to compute total time derivatives dt Z of the observed radar reflectivity factors Z. As illustrated using Next-Generation Radar measurements in a blizzard coupled with an atmospheric river in California, the maps of dt Z reveal features about locations of sources and sinks of condensed water, which are, otherwise, not visible in the maps of Z alone. Using numerical simulations of the blizzard and a radiative-transfer model to forward calculate the corresponding reflectivity factors Z in the S-band, we show the robust correlation between dt Z and the moistening of the troposphere.

R. Wang;Cheng Hu;Y. Li;S. E. Hobbs;W. Tian;X. Dong;L. Chen; "Joint Amplitude-Phase Compensation for Ionospheric Scintillation in GEO SAR Imaging," vol.55(6), pp.3454-3465, June 2017. The ionospheric scintillation induced by local ionospheric plasma anomalies could lead to significant degradation for geosynchronous earth orbit synthetic aperture radar (SAR) imaging. As radar signals pass through the ionosphere with locally variational plasma density, the signal amplitude and phase fluctuations are induced, which principally affect the azimuthal pulse response function. In this paper, the compensation of signal amplitude and phase fluctuations is studied. First, space-variance problem of scintillation is addressed by image segmentation. Then, SPECAN imaging algorithm is adopted for each image segment, because it is computationally efficient for small imaging scene. Furthermore, an iterative algorithm based on entropy minimum is derived to jointly compensate the signal amplitude and phase fluctuations. Finally, a real SAR scene simulation is used to validate our proposed method, where both the simulated scintillation using phase screen technique and the real GPS-derived scintillation data are adopted to degrade the imaging quality.

A. R. Ragi;Maithili Sharan;Z. S. Haddad; "Objective Detection of Indian Summer Monsoon Onset Using QuikSCAT Seawinds Scatterometer," vol.55(6), pp.3466-3474, June 2017. Surface winds from the QuikSCAT scatterometer over a region parallel to southern peninsular (SP) India are analyzed to monitor the daily evolution of the monsoon. For this purpose, the daily flow direction (DFD) and the corresponding optimal angle are derived from scatterometer winds for the period 2003-2009. These are correlated with the modern era retrospective analysis for research and application reanalysis land surface temperatures averaged over the SP. In the time series for DFD, positive DFD period is wet monsoon with a well-defined onset, followed by a short wet-to-dry transition where DFD is mostly negative and decreasing, then negative DFD period is dry monsoon, and followed by a dry-to-wet transition during which the sign and amplitude of DFD alternates daily between positive and negative. The correlation of DFD with averaged land surface temperatures shows that maximum temperature is achieved during the dry-to-wet transition season and is more pronounced in the years 2004-2007. Though 2005 and 2006 have the longest period of precursor cooling, in 2008 the land surface temperature continues to increase right up to the onset of monsoon. The hypotheses derived from these are: every year, the land surface cooling begins weeks before the monsoon onset over Kerala (MOK); the potential for convection over land is most favorable where DFD is positive, and is inhibited when negative. This study also reveals that one can extract the onset dates with a standard deviation of 3-4 days from these time series data sets, and the latter are used to objectively define the MOK.

Shilong Sun;Bert Jan Kooij;Alexander G. Yarovoy; "Linearized 3-D Electromagnetic Contrast Source Inversion and Its Applications to Half-Space Configurations," vol.55(6), pp.3475-3487, June 2017. One of the main computational drawbacks in the application of 3-D iterative inversion techniques is the requirement of solving the field quantities for the updated contrast in every iteration. In this paper, the 3-D electromagnetic inverse scattering problem is put into a discretized finite-difference frequency-domain scheme and linearized into a cascade of two linear functionals. To deal with the nonuniqueness effectively, the joint structure of the contrast sources is exploited using a sum-of-l1 -norm optimization scheme. A cross-validation technique is used to check whether the optimization process is accurate enough. The total fields are, then, calculated and used to reconstruct the contrast by minimizing a cost functional defined as the sum of the data error and the state error. In this procedure, the total fields in the inversion domain are computed only once, while the quality and the accuracy of the obtained reconstructions are maintained. The novel method is applied to ground-penetrating radar imaging and through-the-wall imaging, in which the validity and the efficiency of the method are demonstrated.

Yuanguo Zhou;Linlin Shi;Na Liu;Chunhui Zhu;Yuefeng Sun;Qing Huo Liu; "Mixed Spectral-Element Method for Overcoming the Low-Frequency Breakdown Problem in Subsurface EM Exploration," vol.55(6), pp.3488-3500, June 2017. One fundamental difficulty in low-frequency subsurface electromagnetic exploration is the low-frequency breakdown phenomenon in numerical computation. It makes the discretized linear system very poorly conditioned and thus difficult to solve. This issue is present in both integral equation and partial differential equation solution methods, and thus has attracted many researchers who have proposed various methods to overcome this difficulty. In this paper, we propose a new mixed spectral element method (mixed SEM) to eliminate this low-frequency breakdown problem and apply this method to solve the subsurface electromagnetic exploration problem. Since Gauss' law is now explicitly enforced in the mixed SEM to make the system matrix well-conditioned even at extremely low frequency, we can solve the linear system from dc to high frequencies. With the proposed method, we study the surface-to-borehole electromagnetic system for hydrocarbon exploration. Numerical examples show that the mixed SEM is accurate and efficient, and has significant advantages over conventional methods.

Ji-An Wei;Difeng Wang;Fang Gong;Xianqiang He;Yan Bai; "The Influence of Increasing Water Turbidity on Sea Surface Emissivity," vol.55(6), pp.3501-3515, June 2017. High-precision measurement of sea surface temperature (SST) requires an accurate knowledge of sea surface emissivity (SSE). Many studies have found that the SST estimations in the coastal areas are less accurate than that in open seas, where water turbidity is negligible. Previous works regard SSE as a function of surface roughness and observation angle; however, works have rarely focused on the internal characteristic of water, such as its turbidity. Thus, this paper presents thermal infrared measurements of the emissivity of turbid water carried out under controlled conditions with a multichannel radiometer working in the 8-14-μm region. The results showed that measured emissivity values decreased with the increase of water turbidity. The decrease was tiny for lower suspended particulate matter (SPM) concentrations but a significance emissivity decrease with higher concentrations, especially at large observation angles. For instance, the difference between concentrations of 0 and 5000 mg/L manifested an average emissivity variation of 2.93% with 5000 mg/L at a viewing angle of 55°, depending on the radiometer spectral channels. And, a parametric relationship of emissivity in terms of SPM concentration was established in this paper. The impact of ignoring water turbidity on SST, using SST algorithms and a case of satellite retrievals, was analyzed. It was indicated that there would be an SST error lower than 0.2 °C at SPM concentrations less than 1000 mg/L and that SST deviations would reach values up to almost 0.5 °C-0.6 °C at 5000 mg/L, even higher than 1 °C at large angles for a given atmospheric water vapor content less than 4 g/cm2.

Ping Zhong;Zhiqiang Gong;Shutao Li;Carola-Bibiane Schönlieb; "Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification," vol.55(6), pp.3516-3530, June 2017. In the literature of remote sensing, deep models with multiple layers have demonstrated their potentials in learning the abstract and invariant features for better representation and classification of hyperspectral images. The usual supervised deep models, such as convolutional neural networks, need a large number of labeled training samples to learn their model parameters. However, the real-world hyperspectral image classification task provides only a limited number of training samples. This paper adopts another popular deep model, i.e., deep belief networks (DBNs), to deal with this problem. The DBNs allow unsupervised pretraining over unlabeled samples at first and then a supervised fine-tuning over labeled samples. But the usual pretraining and fine-tuning method would make many hidden units in the learned DBNs tend to behave very similarly or perform as “dead” (never responding) or “potential over-tolerant” (always responding) latent factors. These results could negatively affect description ability and thus classification performance of DBNs. To further improve DBN's performance, this paper develops a new diversified DBN through regularizing pretraining and fine-tuning procedures by a diversity promoting prior over latent factors. Moreover, the regularized pretraining and fine-tuning can be efficiently implemented through usual recursive greedy and back-propagation learning framework. The experiments over real-world hyperspectral images demonstrated that the diversity promoting prior in both pretraining and fine-tuning procedure lead to the learned DBNs with more diverse latent factors, which directly make the diversified DBNs obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.

Haixia Bi;Jian Sun;Zongben Xu; "Unsupervised PolSAR Image Classification Using Discriminative Clustering," vol.55(6), pp.3531-3544, June 2017. This paper presents a novel unsupervised image classification method for polarimetric synthetic aperture radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, we design an energy function for unsupervised PolSAR image classification by combining a supervised softmax regression model with a Markov random field smoothness constraint. In this model, both the pixelwise class labels and classifiers are taken as unknown variables to be optimized. Starting from the initialized class labels generated by Cloude-Pottier decomposition and $K$ -Wishart distribution hypothesis, we iteratively optimize the classifiers and class labels by alternately minimizing the energy function with respect to them. Finally, the optimized class labels are taken as the classification result, and the classifiers for different classes are also derived as a side effect. We apply this approach to real PolSAR benchmark data. Extensive experiments justify that our approach can effectively classify the PolSAR image in an unsupervised way and produce higher accuracies than the compared state-of-the-art methods.

Haitao Yin; "A Joint Sparse and Low-Rank Decomposition for Pansharpening of Multispectral Images," vol.55(6), pp.3545-3557, June 2017. Pansharpening aims to fuse a high-resolution panchromatic (PAN) image and a low-resolution multispectral (MS) image. Several synthesis techniques have been reported to solve the problem of pansharpening. Details injection (DI) consists of the cascaded processes of details extraction and injection. The former is crucial for performance. By exploiting the relationship among multiple data acquired on the same scene through different sensors, this paper first develops a joint sparse and low-rank (JSLR) decomposition with an assumption that multiple data have a common low-rank component. Then, a novel DI-type pansharpening method is proposed based on JSLR decomposition, named as JSLR-based pansharpening (JSLRP). In JSLRP, the injected spatial details are calculated as a linear combination of JSLR decomposed components. To ensure the low-rank condition, the JSLR is implemented on the PAN and MS images in the nonlocal similar patches form by adopting the nonlocal self-similarity. Finally, the superiority of JSLRP is demonstrated by comparing with several well-known methods on the reduced-scale data and full-scale data.

Wenjun Tang;Kun Yang;Zhian Sun;Jun Qin;Xiaolei Niu; "Global Performance of a Fast Parameterization Scheme for Estimating Surface Solar Radiation From MODIS Data," vol.55(6), pp.3558-3571, June 2017. A fast parameterization scheme named SUNFLUX is first used in this paper to estimate instantaneous surface solar radiation (SSR) based on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard both Terra and Aqua platforms. The scheme mainly takes into account the absorption and scattering processes due to clouds, aerosols, and gas in the atmosphere. The estimated instantaneous SSR is evaluated against surface observations obtained from seven stations of the surface radiation budget network (SURFRAD), four stations in the North China Plain (NCP) and 40 stations of the baseline surface radiation network (BSRN). The statistical results for evaluation against these three data sets show that the relative root-mean-square error (RMSE) values of SUNFLUX are less than 15%, 16%, and 17%, respectively. Daily SSR is derived through temporal upscaling from the MODIS-based instantaneous SSR estimates, and is validated against surface observations. The relative RMSE values for daily SSR estimates are about 16% at the seven SURFRAD stations, four NCP stations, 40 BSRN stations, and 90 China Meteorological Administration (CMA) radiation stations. The accuracy of the scheme is generally higher than those of previous algorithms, and thus can be potentially applied on geostationary satellites for mapping high-resolution SSR data in the future.

Xuebo Zhang;Haining Huang;Wenwei Ying;Huakui Wang;Jun Xiao; "An Indirect Range-Doppler Algorithm for Multireceiver Synthetic Aperture Sonar Based on Lagrange Inversion Theorem," vol.55(6), pp.3572-3587, June 2017. This paper presents a novel imaging algorithm which has the capacity of processing multireceiver synthetic aperture sonar (SAS) data based on Vu's spectrum. There are two important issues when developing the fast Fourier-based imaging algorithms. One is the point target reference spectrum (PTRS) with analytical expression in the 2-D frequency domain; the other is the space variance of the PTRS. Vu has solved the first issue based on the Lagrange inversion theorem. However, the second issue has not been solved yet. Our approach in this paper is to apply Vu's spectral result in an indirect range-Doppler (R-D) imaging algorithm. For each transmitter/receiver pair, the close-form azimuth modulation is derived first, and then, range/azimuth coupling can be obtained via the difference between the PTRS and the azimuth modulation. Unlike traditional R-D algorithm, which utilizes the interpolation operation to eliminate the range cell migration (RCM), the RCM correction (RCMC) in this paper is carried out through two steps, i.e., bulk RCMC and differential RCMC. Bulk RCMC accounts for the RCMC in one reference range. Differential RCMC accomplished with a range-dependent subblock postprocessing method is used to compensate for the space variant deviation phase error. Via the proposed method, the echoed signal corresponding to each transmitter/receiver pair is processed first. Following which, a high-resolution SAS complex image is obtained by coherent superposition of all the coarse subimages. Numerical simulations and real data processing results show that the proposed method has the similar focusing capabilities with the accurate back projection algorithm across the whole swath. Although the presented method has the disadvantage of latency, it is very easy to make a complete phase center approximation correction, allowing for the tandem nature of the collected data. Besides, the proposed algorithm can handle general case with explicit point of stationary phas- (PSP) no matter how complicated the PSP is, and it is also suitable for the imagery of tandem synthetic aperture radar.

Mark Berman;Leanne Bischof;Ryan Lagerstrom;Yi Guo;Jon Huntington;Peter Mason;Andrew A. Green; "A Comparison Between Three Sparse Unmixing Algorithms Using a Large Library of Shortwave Infrared Mineral Spectra," vol.55(6), pp.3588-3610, June 2017. The comparison described in this paper has been motivated by two things: 1) a “spectral library” of shortwave infrared reflectance spectra that we have built, consisting of the spectra of 60 nominally pure materials (mostly minerals, but also water, dry vegetation, and several man-made materials) and 2) the needs of users in the mining industry for the use of fast and accurate unmixing software to analyze tens to hundreds of thousands of spectra measured from drill core or chips using HyLogging instruments, and other commercial reflectance spectrometers. Individual samples are typically a mixture of only one, two, three, or occasionally four minerals. Therefore, in order to avoid overfitting, a sparse unmixing algorithm is required. We compare three such algorithms using some real world test data sets: full subset selection (FSS), sparse demixing (SD), and L1 regularization. To aid the comparison, we introduce two novel aspects: 1) the simultaneous fitting of the low frequency background with mineral identification (which provides greater model flexibility) and 2) the combined fitting being carried out using a suitably defined Mahalanobis distance; this has certain optimality properties under an idealized model. Together, these two innovations significantly improve the accuracy of the results. FSS and L1 regularization (suitably optimized) produce similar levels of accuracy, and are superior to SD. Discussion includes possible improvements to the algorithms, and their possible use in other domains.

Mi Jiang;Zelang Miao;Paolo Gamba;Bin Yong; "Application of Multitemporal InSAR Covariance and Information Fusion to Robust Road Extraction," vol.55(6), pp.3611-3622, June 2017. Automatic road extraction from synthetic aperture radar (SAR) imagery has been studied with success in the past two decades. However, a method that combines full interferometric SAR (InSAR) information is as yet missing. In this paper, we present an algorithm toward robust road extraction by fully exploring the multitemporal InSAR covariance matrix. To improve the detection performance and reduce false alarm ratio, intensity and coherence are first accurately estimated without loss of image resolution by homogeneous pixel selection and robust estimators. After the identification of road candidates from each quantity using multiscale line detectors, novel information fusion rules are applied to integrate the extracted results and generate the final road network. The method is tested and quantitatively evaluated on TerraSAR-X data sets depicting two scenes where complex road features make it hard for standard SAR-based methods. The experimental results show that the new method can achieve satisfactory detection performances.

Zhenwei Shi;Zhengxia Zou; "Can a Machine Generate Humanlike Language Descriptions for a Remote Sensing Image?," vol.55(6), pp.3623-3634, June 2017. This paper investigates an intriguing question in the remote sensing field: “can a machine generate humanlike language descriptions for a remote sensing image?” The automatic description of a remote sensing image (namely, remote sensing image captioning) is an important but rarely studied task for artificial intelligence. It is more challenging as the description must not only capture the ground elements of different scales, but also express their attributes as well as how these elements interact with each other. Despite the difficulties, we have proposed a remote sensing image captioning framework by leveraging the techniques of the recent fast development of deep learning and fully convolutional networks. The experimental results on a set of high-resolution optical images including Google Earth images and GaoFen-2 satellite images demonstrate that the proposed method is able to generate robust and comprehensive sentence description with desirable speed performance.

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Miao Li;Yanqing Guo;Ming Li;Guoqi Luo;Xiangwei Kong; "Coupled Dictionary Learning for Target Recognition in SAR Images," vol.14(6), pp.791-795, June 2017. In this letter, we propose a novel classification strategy called the coupled dictionary learning for target recognition in synthetic aperture radar (SAR) images. First, we train structured synthesis dictionaries to reflect the difference among each category. Second, we introduce a shared dictionary to reduce the effect of common features, such as the high similarity caused by specular reflection. Finally, we use the analysis dictionary to improve the efficiency of recognition by eliminating the constraint of the l0-norm or l1-norm of sparse code. Experimental results on Moving and Stationary Target Acquisition and Recognition data set indicate that our method can achieve better performance in SAR target recognition than the state-of-the-art methods, such as tritask joint sparse representation and CKLR. Especially, this method can be more robust when the depressions have obvious changes.

Ke Wu;Qian Du; "Subpixel Change Detection of Multitemporal Remote Sensed Images Using Variability of Endmembers," vol.14(6), pp.796-800, June 2017. Due to the existence of mixed pixels in a remote sensed image, traditional change detection (CD) methods at “full-pixel level” are often unable to provide detailed changed information effectively. A subpixel change detection (SCD) technique can deal with this issue with two steps: soft classification is applied to derive proportional differences from coarse multitemporal images, and then a sharpened thematic map with fine spatial resolution is generated based on subpixel mapping. However, changes in endmember combination within pixels are ignored, which can result in flawed differences and degraded accuracy of SCD. The aim of this letter is to present a new SCD algorithm using variability of endmembers (SCD_VE), where a simple but effective model is proposed to take into consideration the real change of endmember combination. In order to evaluate the performance of the new algorithm, experiment is conducted on simulated images. Experimental results demonstrated that the proposed SCD_VE offers better performance than traditional SCD methods in providing more detailed CD map.

Alexander Marx;Gideon Okpoti Tetteh; "A Forest Vitality and Change Monitoring Tool Based on RapidEye Imagery," vol.14(6), pp.801-805, June 2017. Forest damage and health problems can be identified, mapped, and better managed through the analysis of satellite imagery. However, a high degree of efficiency is achieved only if a workflow for preprocessing and analysis is mature enough for production and can be deployed in an operational processing environment. This letter shows a path for turning research into an operational tool for the purposes of forest delineation and disturbance monitoring using high-resolution RapidEye satellite imagery. The implemented workflow follows a modularized approach with separate modules for image registration, radiometric normalization, classification, and bitemporal analysis. The tool was developed for commercial service provision. It has successfully been deployed for the fourth year for German state forest customers. It makes use of python and python libraries, as well as algorithms of PCI Geomatica. The letter concludes with a case study showing the defoliation monitoring results of a Scots pine plantation area affected by a nun moth outbreak.

Hao Ding;Jixian Zhang;Guoman Huang;Jianjun Zhu; "An Improved Multi-Image Matching Method in Stereo-Radargrammetry," vol.14(6), pp.806-810, June 2017. The new generation of synthetic aperture radar (SAR) sensors provides us with an opportunity to match multiple high-resolution SAR images. Moreover, the multiple SAR image matching methods have recently gained a lot of attention due to the fact that they can obtain more accurate, better distributed, and more reliable matches than the stereo matching methods. In this letter, we present an improved multi-image matching method to simultaneously identify matches from multiple SAR amplitude images. The proposed method makes better use of the relationships between the pixels in the deformed correlation window and integrates geometric and radiometric information from multiple SAR images. Experiments on Chinese Academy of Surveying and Mapping Synthetic Aperture Radar (CASMSAR) data sets demonstrate that the improved multi-image matching method is capable of providing more accurate and better distributed matches, as well as offering a better multi-image matching solution in stereo-radargrammetry under the conditions of geometric and radiometric distortions, especially in low-texture areas.

Hui Wang;Zhansheng Chen;Shichao Zheng; "Preliminary Research of Low-RCS Moving Target Detection Based on Ka-Band Video SAR," vol.14(6), pp.811-815, June 2017. Conventional synthetic aperture radar (SAR) ground moving target detection is only effective for targets with high radar cross section (RCS), and its performance is degraded by focusing distortion caused by the motion of the target. This letter proposes a new low-RCS moving target detection method which exploits the target's shadow using high-resolution sequential images captured by a Ka-band video SAR system. First, the characteristics of target shadow are investigated, which are mainly influenced by the size of the target, the incidence of the radar beam, and the target velocity. Then the presented method based on video SAR system is described, and its moving target detection performance is also analyzed. As a preliminary study, a simulated experiment is conducted in which the signal of a low-RCS moving target is simulated and inserted into real airborne Ka-band video SAR images. The results demonstrate the feasibility of the proposed method for low-RCS moving target detection.

Cunzhao Shi;Chunheng Wang;Yu Wang;Baihua Xiao; "Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification," vol.14(6), pp.816-820, June 2017. Ground-based cloud classification is crucial for meteorological research and has received great concern in recent years. However, it is very challenging due to the extreme appearance variations under different atmospheric conditions. Although the convolutional neural networks have achieved remarkable performance in image classification, no one has evaluated their suitability for cloud classification. In this letter, we propose to use the deep convolutional activations-based features (DCAFs) for ground-based cloud classification. Considering the unique characteristic of cloud, we believe the local rich texture information might be more important than the global layout information and, thus, give a comprehensive evaluation of using both shallow convolutional layers-based features and DCAFs. Experimental results on two challenging public data sets demonstrate that although the realization of DCAF is quite straightforward without any use-dependent tricks, it outperforms conventional hand-crafted features considerably.

Jean-Michel Friedt; "Passive Cooperative Targets for Subsurface Physical and Chemical Measurements: A Systems Perspective," vol.14(6), pp.821-825, June 2017. We investigate the use of a commercially available ground-penetrating radar (GPR) for probing the response of subsurface sensors designed as passive cooperative targets. Such sensors must meet two design criteria: introduce a signature unique to the sensor which will hence be differentiated from clutter, and introduce in this signature a response characteristic allowing for recovering the physical quantity under investigation. Using piezoelectric substrates for converting the incoming electromagnetic pulse to an acoustic wave confined to the sensor surface (surface acoustic wave transducer) allows for shrinking the sensor dimensions while providing sensing capability through the piezoelectric substrate acoustic wave velocity dependence with the physical quantity under investigation. Two broad ranges of sensing mechanisms are discussed: intrinsic piezoelectric substrate velocity dependence with a quantity - restricted to the measurement of temperature or strain and, hence, torque or pressure - and extrinsic load dependence on the sensor, allowing for the measurement of variable capacitive or resistive loads. In all cases, the delay introduced by the physical quantity variation induces a phase rotation of the returned signal of a few periods at most, to be measured with a resolution of a fraction of a period: the GPR receiver sampling time reference must exhibit a long term stability at least as good as the targeted phase measurement needed to recover the physical quantity. We show that the commercial GPR exhibits excessive time-base drift, yielding a loss in the sensing capability, while a quartz-oscillator-based alternative implementation of the classical stroboscopic sampling receiver compensates for such a drawback.

Hamid Reza Shahdoosti;Nayereh Javaheri; "Pansharpening of Clustered MS and Pan Images Considering Mixed Pixels," vol.14(6), pp.826-830, June 2017. The component substitution (CS) scheme is one of the most efficient models used by different image fusion algorithms when merging multispectral and panchromatic images, acquired with different spatial and spectral resolutions. In this letter, a new CS-based image fusion method is proposed to reduce color distortion. First, multispectral images are clustered into several classes using spectral and spatial features, and then linear regression with non-negative coefficients is used to calculate summation weights for each class of pixels. To consider mixed pixels not belonging to any distinct class, the proposed method employs the fuzzy c-means algorithm. Qualitative and quantitative results are reported for two data sets, namely, Landsat-7 Enhanded Thematic Mapper Plus and QuickBird. Visual and statistical assessments show the validity of the proposed method.

Saeid Gholinejad;Rouzbeh Shad;Hadi Sadoghi Yazdi;Marjan Ghaemi; "Improving Signal Subspace Identification Using Weighted Graph Structure of Data," vol.14(6), pp.831-835, June 2017. Signal subspace identification (SSI) is known as an important preprocessing for most remote sensing processes and its applications. Hence, a graph-based method is presented in this letter to improve identification of signal subspace and its dimension. Our proposed method reduces sensitivity to noise through integrating a weighted graph of image pixels in the cost function of the well-known hyperspectral SSI by minimum error (HySime) method to improve the accuracy of SSI. The method proposed in this letter is a very simple and yet very effective in estimating various types of hyperspectral data. The proposed method was implemented on various synthetic and real data. The results of the experiments on both types of hyperspectral data indicated the accuracy of this approach in estimating the signal subspace as compared with other well-known methods.

Mengdi Wang;Jing Yu;Lijuan Niu;Weidong Sun; "Feature Extraction for Hyperspectral Images Using Low-Rank Representation With Neighborhood Preserving Regularization," vol.14(6), pp.836-840, June 2017. Hyperspectral images (HSIs) usually contain hundreds of spectral bands. When they are used for classification tasks, HSIs may suffer from the curse of high dimensionality. To address this problem, the essential procedures of dimension reduction and feature extraction (FE) are employed. In this letter, we propose an FE method for HSIs using low-rank representation with neighborhood preserving regularization (LRR_NP). The proposed method can simultaneously employ locally spatial similarity and the spectral space structure, which comprises a union of multiple low-rank subspaces. The framework of LRR can structurally represent the union structure of a spectral space. Because spatial neighbor pixels always share high similarity in a feature space, an NP regularization item is introduced into the framework of LRR to consider the locally spatial correlation. Classification experiments are conducted on real HSI data sets; the results demonstrate that the features that are extracted by LRR_NP are more discriminative than the state-of-art methods, including both unsupervised methods and supervised methods.

Lianjie Qin;Wei Wu;Yugang Tian;Wei Xu; "LiDAR Filtering of Urban Areas With Region Growing Based on Moving-Window Weighted Iterative Least-Squares Fitting," vol.14(6), pp.841-845, June 2017. Terrain surfaces can be relatively accurately obtained from light detection and ranging data using least-squares interpolation. However, the accuracy of the extraction results is low in regions with relatively large terrain undulations, and large buildings and vegetation with large areas and relatively low permeabilities cannot be eliminated. In this letter, we propose a region growing filtering method based on moving-window weighted iterative least-squares fitting. This technique uses the moving-window weighted iterative least squares fitting method to select the seed point. After multiple iterations, we take the difference between the original data and the fitting results and retain the points with differences within the threshold range. We use these points as the seed points and adopt the region growing method to reconstruct the complete point set of ground points. We use five test data sets provided by the International Society for Photogrammetry and Remote Sensing and data from the Iowa River Basin in the United States for experiments. The results indicate that the proposed method can effectively remove buildings and vegetation, but it still requires further improvement for the removal of bridges and objects at the edge.

Vinicius R. N. Santos;Cassiano A Bortolozo;Jorge L. Porsani; "Joint Inversion of Apparent Conductivity and Magnetic Susceptibility to Characterize Buried Targets," vol.14(6), pp.846-850, June 2017. A good knowledge of the underground space is needed in urban planning. The main objects found buried are water and sewage pipes, cables, and contaminants storage tanks. Through the inversion of inductive electromagnetic data, we can obtain parameters like depth, position, and radius of these targets. Here, we show a joint inversion approach of apparent conductivity and magnetic susceptibility using data collected at the IAG/USP test site. The model employed assumes three coils (transmitter, receiver, and target/conductor) and relates the electromotive force, through the mutual inductance that occurs between the pairs of transmitter-receiver and conductor-receiver coils, to the acquired conductivity and susceptibility data. The achieved adjustment between field and calculated data indicates that, despite the simplicity of the model, it is a good representation of the conductors, increasing the results of the inversion process. The inversion algorithm is based on the controlled random search algorithm, which presented to be quite stable with electromagnetic data. The main advantage of the proposed approach is the relatively fast inversion process that showed better results than the traditional qualitative interpretation usually applied to this kind of investigation, being promising for noninvasive target characterization and having application in geotechnical studies.

Guoli Wang;Xinchao Wang;Bin Fan;Chunhong Pan; "Feature Extraction by Rotation-Invariant Matrix Representation for Object Detection in Aerial Image," vol.14(6), pp.851-855, June 2017. This letter proposes a novel rotation-invariant feature for object detection in optical remote sensing images. Different from previous rotation-invariant features, the proposed rotation-invariant matrix (RIM) can incorporate partial angular spatial information in addition to radial spatial information. Moreover, it can be further calculated between different rings for a redundant representation of the spatial layout. Based on the RIM, we further propose an RIM_FV_RPP feature for object detection. For an image region, we first densely extract RIM features from overlapping blocks; then, these RIM features are encoded into Fisher vectors; finally, a pyramid pooling strategy that hierarchically accumulates Fisher vectors in ring subregions is used to encode richer spatial information while maintaining rotation invariance. Both of the RIM and RIM_FV_RPP are rotation invariant. Experiments on airplane and car detection in optical remote sensing images demonstrate the superiority of our feature to the state of the art.

Xiao Pan;Siyuan Cao;Shaohuan Zu;Fei Gong; "SVD-Constrained MWNI With Shaping Theory," vol.14(6), pp.856-860, June 2017. Observed seismic data are mostly irregularly sampled and seismic data interpolation is an essential procedure to provide accurate complete data for seismic data analysis, such as amplitude-versus-offset analysis, multiple suppression, and wave-equation migration. The well-known minimum weighted norm interpolation (MWNI) method could achieve a relatively good result. However, the algorithm needs many iterations and thus the total calculation is expensive. In this letter, we propose a fast interpolation algorithm. Instead of wavenumber spectrum, singular spectrum can give a more accurate description of the sampled data. We use shaping regularization to control the smoothness of the singular values matrix. Compared with the conventional MWNI method, we test the proposed method on both synthetic and field data sets. The results confirm that our proposed method is more effective.

Weitong Ruan;Adam B. Milstein;William Blackwell;Eric L. Miller; "A Probabilistic Analysis of Positional Errors on Satellite Remote Sensing Data Using Scattered Interpolation," vol.14(6), pp.861-865, June 2017. With the recent development of CubeSats, several ultracompact, low cost, and rapidly deployable satellites have been developed for earth observation missions. Because of the geometry of the acquisition process, measurements are irregularly sampled, whereas in meteorological applications, data are preferred on a regular grid. This problem is further complicated by the fact that, due to CubeSats' compact sizes and constraints, such as limited power, errors occur in geolocation calibration, resulting in positional errors. In this letter, we analyze how the commonly used triangulation-based linear data interpolation scheme behaves under probabilistic models for the positional errors. The derived distribution of interpolation error caused by positional error is intractable even under a Gaussian distribution for positional errors. To address this problem, we developed an analytical closed-form solution to the first two moments of the interpolation error. Using models for positional errors motivated by our prior work, experimental results show that, compared with the first-order linear model, the second-order one provides a better approximation in terms of the mean and variance, which is very close to that is obtained using more computationally intensive Monte Carlo simulations. This model also allows for the closed-form calculation of mean squared interpolation error, which can be of use in the context of system design where the impact of positional errors on remote sensing products must be considered.

Erik Haß;David Frantz;Sebastian Mader;Joachim Hill; "Global Analysis of the Differences Between the MODIS Vegetation Index Compositing Date and the Actual Acquisition Date," vol.14(6), pp.866-870, June 2017. Image compositing enables the generation of continuous and equidistant time series of vegetation indices (VIs) as a requisite for deriving phenological metrics. This is achieved by selecting a representative choice from available observations within a compositing period. Commonly, phenological processors associate the VI value with a reference date rather than the actual acquisition date. This letter focuses on a global assessment of the resulting temporal discrepancy between the assumed reference date and the actual acquisition date using Moderate Resolution Imaging Spectroradiometer (MODIS)-VI day of composite (DOC) information from 2000 to 2015. It is shown that the long-term global mean DOC coincides with the midday of a compositing period (day 8.44 ± 0.44). The global summarized 2-D frequency distributions of succeeding compositing periods revealed that only 16.93% of all occurrences indicate a 16-day difference. The commonly used assumptions that the DOC coincides with the first, mid, or last day of the compositing period on average only hold true for 7.02%, 6.07%, and 6.03% of temporal succeeding compositing periods, respectively. Spatial patterns of 2-D frequency distributions occur as a result of climatic regions and seasonality. A MODIS tile is on average composed of more than 50% DOC combinations, indicating deviations (1-31 days) from the 16-day difference. Since information about the actual acquisition date is available, and methods for the incorporation of irregular data into phenological analysis do exist, it is recommended to incorporate this information into the phenology research based on VI time series to avoid unnecessary uncertainty.

Andre Theron;Jeanine Engelbrecht;Jaco Kemp;Waldo Kleynhans;Terence Turnbull; "Detection of Sinkhole Precursors Through SAR Interferometry: Radar and Geological Considerations," vol.14(6), pp.871-875, June 2017. Sinkholes are an unpredictable geohazard that endanger life and property in dolomitic terrains. Sinkholes are a significant threat in Gauteng, South Africa's most populated and urbanized province. Small-scale surface subsidence is frequently present prior to the collapse of a sinkhole. Therefore, the presence of precursory surface deformation can be exploited to develop early warning systems. Spaceborne synthetic aperture radar (SAR) differential interferometry (DInSAR) is able to monitor small-scale surface deformation over large areas and can be used to detect and measure precursors to sinkhole development. This letter investigates the use of repeat-pass DInSAR to detect sinkhole precursors in the Gauteng province. Twenty stripmap acquisitions from TerraSAR-X were acquired over a full year. DInSAR results revealed the presence of three previously unknown deformation features, one of which could be confirmed by subsequent field investigations. Furthermore, a water supply pipeline ruptured six months after the initial observation. The detection of the deformation, therefore, provided a viable early warning to landowners who were unaware of the subsidence. Detected deformation features were between 40 and 100 m in diameter. The maximum displacement measured was 50 mm over 55 days. Despite the successful detection, seven sinkhole events occurred in the observation period, for which no deformation could be detected. The results indicate that high-resolution X-band interferometry is able to monitor dolomite-induced instability in an urban environment. However, considerations related to SAR interferometry and physical sinkhole properties need to be addressed before DInSAR can be used in an operational early warning system.

Peng-Bo Wei;Min Zhang;Jin-Xing Li;Yong-Chang Jiao; "Space-Time Correlated Sea Scattering Map Simulation Based on Statistical Model," vol.14(6), pp.876-880, June 2017. In numerous researching fields involving sea scattering problems, the electromagnetic (EM) scattering model becomes a better alternative rather than a direct experiment due to its low cost and easy realization. Nevertheless, the EM scattering model still appears to be slow and laborious when encountering the large scene or the long time case. This letter proposes an effective and efficient approach for 3-D space-time correlated sea scattering map simulation, an important step in real clutter simulation, which is based on the statistical characteristics from the EM scattering model. Namely, this approach can give a fast simulation of the large scene or long time space-time correlated sea scattering map through the statistic characteristics of that with a small scene or short time acquired by the EM scattering model. From the comparisons of the texture feature and the statistical characteristics between the results derived from the EM model and the proposed approach, it is demonstrated that the proposed approach can give a fast and effective simulation of the 3-D space-time correlated sea scattering map.

Yuchen Wang;Wenkai Lu;Benfeng Wang; "An Events Rearrangement Strategy-Based Robust Principle Component Analysis," vol.14(6), pp.881-885, June 2017. Random noise in seismic data can affect the performance of reservoir characterization and interpretation, which makes denoising become an essential procedure. This letter focuses on suppressing random noise in poststack seismic data while preserving the edges of desired signals. Due to the lateral continuity of seismic data, polynomial fitting (PF) method can be a good alternative in attenuating random noise. However, discontinuities exist widely in poststack seismic data, which might be damaged by the PF filter. By contrast, principle component analysis (PCA)-based filters have better performance in edge preserving, but there appear artifacts in the denoised results using the PCA-based filters. Thus, we propose an edge-preserving polynomial PCA filter which combines advantages of the PF and PCA methods by optimizing a PCA problem with a weighted polynomial constraint. The weight coefficient is determined adaptively according to the signal-to-noise ratio estimation and the energy proportion in the selected analysis window, which can help distinguish the horizontal continuous events and the edges effectively. To deal with the complicated slopes which make the local linear hypothesis invalid, we introduce a robust local slope estimation method and apply the slope estimation-based event tracing strategy to horizontally align the data set. Synthetic and field data examples show that the proposed method has a better performance in noise attenuation and edge preserving, compared with the edge-preserving PF method. In addition, the denoised results are free from artifacts.

Delwyn Moller;Konstantinos M. Andreadis;Kat J. Bormann;Scott Hensley;Thomas H. Painter; "Mapping Snow Depth From Ka-Band Interferometry: Proof of Concept and Comparison With Scanning Lidar Retrievals," vol.14(6), pp.886-890, June 2017. This letter presents the first demonstration of millimeter-wave single-pass interferometric synthetic aperture radar (InSAR) for snow-depth mapping. Maps are presented over the Tuolumne River Basin region of the Sierra Nevada, CA, USA, and compared with those collected by a scanning lidar onboard the NASA Airborne Snow Observatory for the same region on the same snow day. For this observation, the snow surface was wet and melting and as such penetration of the electromagnetic wave into the snow volume can be effectively neglected. Despite the rugged terrain, heavy tree-cover, and very low snow-volume, depth maps had a standard deviation <;1 m with the largest differences occurring on slopes exceeding 40°. While additional evaluation is needed with demonstration of the InSAR capability over a greater range of conditions and terrain, these results are promising. InSAR for snow-depth mapping holds significant advantages for a spaceborne mission if proven viable as it can operate through cloud cover, day or night, and measure snowpack when wet or melting.

Shengxiu Zhou;Yunkai Deng;Robert Wang;Ning Li;Qi Si; "Effective Mapping of Urban Areas Using ENVISAT ASAR, Sentinel-1A, and HJ-1-C Data," vol.14(6), pp.891-895, June 2017. This letter presents a methodology for urban area mapping with density-based spatial clustering of applications with noise (DBSCAN) using the Advanced Synthetic Aperture Radar (ASAR), Sentinel-1A, and HuanJing-1C data. Urban areas have a diversity of shapes, including circles, squares, strips, and other irregular shapes, and the DBSCAN clustering algorithm is suitable for identifying clusters of arbitrary shapes. Exploiting DBSCAN to extract urban areas is a key aspect of this method, and improvements via the incorporation of synthetic aperture radar data preprocessing and postprocessing also play important roles in optimizing the extractions. Different test site sizes were chosen to demonstrate the effectiveness and feasibility of the proposed method, and the validation results showed that the method is efficient and accurately extracts urban areas ranging from small towns to super metropolitan areas.

Adeline Bailly;Laetitia Chapel;Romain Tavenard;Gustau Camps-Valls; "Nonlinear Time-Series Adaptation for Land Cover Classification," vol.14(6), pp.896-900, June 2017. Automatic land cover classification from satellite image time series is of paramount relevance to assess vegetation and crop status, with important implications in agriculture, biofuels, and food. However, due to the high cost and human resources needed to characterize and classify land cover through field campaigns, a recurrent limiting factor is the lack of available labeled data. On top of this, the biophysical-geophysical variables exhibit particular temporal structures that need to be exploited. Land cover classification based on image time series is very complex because of the data manifold distortions through time. We propose the use of the kernel manifold alignment (KEMA) method for domain adaptation of remote sensing time series before classification. KEMA is nonlinear and semisupervised and reduces to solve a simple generalized eigenproblem. We give empirical evidence of performance through classification of biophysical (leaf area index, fraction of absorbed photosynthetically active radiation, fractional vegetation cover, and normalized difference vegetation index) time series on a global scale.

Swapna Raghunath;D. Venkata Ratnam; "Maximum–Minimum Eigen Detector for Ionospheric Irregularities Over Low-Latitude Region," vol.14(6), pp.901-905, June 2017. Plasma irregularities are a predominant feature over the low-latitude ionosphere within 20° north and south of the geomagnetic equator. The range delay errors introduced in the global navigation satellite system (GNSS) and in the space-based augmentation system by the plasma irregularities are difficult to measure due to the unpredictable nature of ionosphere. This letter presents a maximum-minimum eigen algorithm that has been developed for the efficient detection of ionospheric irregularities in the low latitudes. GNSS data were collected from five stations spread over the Indian terrain for the solar maximum year of 2013 and real-time detection of plasma irregularities was performed. The five GNSS stations, namely, Pbr2, Iisc, Guntur, Hyde, and Lck2 span over the geomagnetic latitudes ranging from 2.07° N to 17.92° N. The results show a very good correlation with the equatorial ionospheric disturbances. The occurrence of plasma irregularities was found to be a maximum at the ionospheric anomaly crest.

Yanfeng Gu;Qingwang Wang; "Discriminative Graph-Based Fusion of HSI and LiDAR Data for Urban Area Classification," vol.14(6), pp.906-910, June 2017. A novel discriminative graph-based fusion (DGF) method is proposed for urban area classification to fuse heterogeneous features from two data sources, i.e., hyperspectral image (HSI) and light detecting and ranging (LiDAR) data. The features include spectral characteristics in HSI, height in LiDAR data, and geometry in image processing technologies like morphological profiles (MPs). Our proposed DGF method couples dimension reduction and heterogeneous feature fusion. The core idea of the proposed method is to search for a projection matrix by minimizing the similarity term that preserves the local geometry of each class and maximizing the dissimilarity term that contains the relation of between-class distance. As a result, the proposed method can pull close together samples of the same class while pushing those of different classes apart in the projected space by fusing graphs constructed by different groups of heterogeneous features. The edges of the graphs are measured by kernel. Furthermore, the multiscale DGF (MS-DGF) is introduced to utilize the capability of similarity measure of different scales of kernel and avoid finding the optimal scale simultaneously. Experiments are conducted on real HSI along with LiDAR data. The corresponding results demonstrate that the proposed method can make an effective fusion of heterogeneous features to make full use of the complementary information of HSI and LiDAR, which facilitates fine classification task of urban area, compared with several state-of-the-art algorithms.

Zhixin Zhang;Yun Shao;Wei Tian;Qiufang Wei;Yazhou Zhang;Qingjun Zhang; "Application Potential of GF-4 Images for Dynamic Ship Monitoring," vol.14(6), pp.911-915, June 2017. The successful launch of the GF-4 satellite has greatly enhanced wide-swath and high-frequency imaging capabilities, both of which are urgently needed in the fields of ship detection and monitoring. In this letter, the applicability of GF-4 satellite data for ship recognition and maritime traffic surveillance was investigated. It is concluded that the movement of ships, the velocity and direction, for instance, can be detected by using either a single GF-4 image that has short time lags between bands, or time-series GF-4 images that have a short interval between their acquisition times, implying that GF-4 satellite images have the potentiality for the monitoring of moving ships.

Huaguo Huang;Wenhan Qin;Robert J. D. Spurr;Qinhuo Liu; "Evaluation of Atmospheric Effects on Land-Surface Directional Reflectance With the Coupled RAPID and VLIDORT Models," vol.14(6), pp.916-920, June 2017. In order to assess atmospheric effects on the directional reflectance of land surface, we have developed a new approach coupling the 3-D radiosity-based land-surface model [radiosity applicable to porous individual objects (RAPID)] with the atmospheric radiative transfer (RT) model [vector linearized discrete ordinate RT (VLIDORT)]. RAPID is used to generate a lookup table of bidirectional reflectance distribution function (BRDF) elements required by VLIDORT for the surface boundary condition. To test the RAPID-VLIDORT model, we used five natural 3-D scenes along with five aerosol optical depths (AODs). Results for top-of-atmosphere radiances show semiempirical analytical BRDF models are insufficiently accurate to represent bidirectional reflectance factors (BRFs) in hotspot regions and over wide angular variations. The large impact of AOD on BRF hotspot also underlines the importance of precise atmospheric corrections for multiangular remote sensing of the earth's surface.

Yi Zhong;Yang Yang;Xi Zhu;Eryk Dutkiewicz;Zheng Zhou;Ting Jiang; "Device-Free Sensing for Personnel Detection in a Foliage Environment," vol.14(6), pp.921-925, June 2017. In this letter, the possibility of using device-free sensing (DFS) technology for personnel detection in a foliage environment is investigated. Although the conventional algorithm that based on statistical properties of the received-signal strength (RSS) for target detection at indoor or open-field environment has come a long way in recent years, it is still questionable if this algorithm is fully functional at outdoor with the changing atmosphere and ground conditions, such as a foliage environment. To answer this question, a variety of the measured data have been taken using different targets in a foliage environment. Applying these data along with support vector machine, the impact on detection accuracy due to different classification algorithms is studied. An algorithm that based on the extraction of the high-order cumulant (HOC) of the signals is presented, while the conventional RSS-based one is used as a benchmark. The measurement results show that the classification accuracy of the HOC-based algorithm is better than the RSS-based one by at least 17%. Moreover, to ensure the reliability of the HOC-based approach, the impact on classification accuracy due to different numbers of training samples and different values of signal-to-noise ratio is extensively verified using experimentally recorded samples. To the best of our knowledge, this is the first time that a DFS-based sensing approach is demonstrated to have a potential to distinguish between human and small-animal targets in a foliage environment.

Bo Liu;Kan Tang;Jian Liang; "A Bottom-Up/Top-Down Hybrid Algorithm for Model-Based Building Detection in Single Very High Resolution SAR Image," vol.14(6), pp.926-930, June 2017. Building detection from high-resolution synthetic aperture radar (SAR) image is an essential issue for many SAR applications in urban areas. In this letter, we propose a novel bottom-up/top-down hybrid algorithm for model-based building detection from single very high resolution (VHR) SAR image. First, the building model is generated and described by a set of extraction criteria, which restrict the spatial layout of a building and its primitive features. Specifically, the rectangles of different intensity levels are extracted from the SAR image as primitive features. Then the bottom-up stage proposes building candidates composed by extracted rectangles, and the top-down step predicts building candidates composed by weak features omitted in the primitive extraction. After that, all candidates are verified through false alarm detection. Under this framework, the detection performances can be greatly improved especially in dense built-up areas. The effectiveness of the proposed method is verified by experimental results obtained from real VHR SAR images.

Lilong Qin;Sergiy A. Vorobyov;Zhen Dong; "Joint Cancelation of Autocorrelation Sidelobe and Cross Correlation in MIMO-SAR," vol.14(6), pp.931-935, June 2017. Waveform separation based on matched filtering leads to autocorrelation sidelobe and cross correlation, which deteriorate the performance of multiple-input multiple-output synthetic aperture radar (MIMO-SAR). This letter investigates the performance of a waveform-separation approach employing an extended space-time coding (STC) scheme for MIMO-SAR. Using the autocorrelation property of multiphase complementary codes, we propose a novel STC scheme to effectively cancel out both the autocorrelation sidelobe and cross correlation. Using theoretical analysis also confirmed by simulations, we show that the proposed scheme decreases the sidelobe ratio while increasing the signal-to-noise ratio, leading to high-quality.

Wenkai Li;Kun Fu;Hao Sun;Xian Sun;Zhi Guo;Menglong Yan;Xinwei Zheng; "Integrated Localization and Recognition for Inshore Ships in Large Scene Remote Sensing Images," vol.14(6), pp.936-940, June 2017. Automatic inshore ship recognition, which includes target localization and type recognition, is an important and challenging task. However, existing ship recognition methods mainly focus on the classification of ship samples or clips. These methods rely deeply on the detection algorithm to complete localization and recognition in large scene images. In this letter, we present an integrated framework to automatically locate and recognize inshore ships in large scene satellite images. Different from traditional object recognition methods using two steps of detection-classification, the proposed framework could locate inshore ships and identify types without the detection step. Considering ship size is a useful feature, a novel multimodel method is proposed to utilize this feature. And an Euclidean-distance-based fusion strategy is used to combine candidates given by models. This fusion strategy could effectively separate side-by-side ships. To handle large scene images efficiently, scale-invariant feature transform registration is also integrated into the framework to utilize geographic information. All of these make the framework an end-to-end fashion which could automatically recognize inshore ships in large scene satellite images. Experiments on Quickbird images show that this framework could achieve the actual applied requirements.

Long-Gang Wang;Lianlin Li;Jun Ding;Tie Jun Cui; "A Fast Patches-Based Imaging Algorithm for 3-D Multistatic Imaging," vol.14(6), pp.941-945, June 2017. Three-dimensional multistatic imaging is a powerful noninvasive examination tool for many military and civilian applications. Recently, the sparsity-regularized optimization has been used as a popular imaging technique to enhance the image quality. However, it suffers from the expensive computational cost, since its solution is obtained by a time-consuming iterative scheme, which is typically computationally prohibitive for large-scale imaging problems. To overcome this difficulty, this challenging imaging problem is converted into an image processing problem in this letter, which can be performed over small-scale overlapping patches and be efficiently solved in a parallel or distributed manner. In this way, the proposed qualitative scheme could be utilized to solve large-scale imaging problems. Exemplary simulation results are provided to demonstrate the efficiency of the proposed methodology.

Ying Luo;Yong-an Chen;Yu-xue Sun;Qun Zhang; "Narrowband Radar Imaging and Scaling for Space Targets," vol.14(6), pp.946-950, June 2017. Based on the narrowband radar, an imaging method for space targets is proposed in this letter, which is named as single-range interferometric imaging. First, the theory of space target echo signal interferometric processing in narrowband radar is explained. Then, through conducting short-time Fourier transform on the echo signal received by three antennas, the curves on time-frequency plane correspond to different scatterers are effectively extracted and separated and the interferometric phase of different scatterers is obtained. Finally, 2-D imaging for space target is realized. Compared to existing methods, only a single multiantenna radar is needed to obtain the 2-D image of target with accurate scaling result. The simulation results under different occasions have confirmed the effectiveness of the proposed method.

Peng-Cheng Yang;Xiao-De Lyu;Zhi-Hai Chai;Dan Zhang;Qi Yue;Jing-Mao Yang; "Clutter Cancellation Along the Clutter Ridge for Airborne Passive Radar," vol.14(6), pp.951-955, June 2017. This letter examines the problem of clutter cancellation in airborne passive radar. This problem is exacerbated by the Doppler spread of clutter due to the receiver motion, since targets falling in the clutter Doppler band will be canceled. To address this problem, a least mean square-based algorithm is proposed for the sidelooking airborne passive radar. Making use of the directional dependence of the clutter Doppler frequency, this algorithm cancels clutter along the clutter ridge and thus avoids the cancellation of targets. Simulations with the experimental data show that clutter can be canceled ideally with the proposed algorithm and signal-to-noise ratio losses are slight, for example, the losses are within 1 dB for targets with radial velocity greater than 10 m/s.

Peter Planinšiĉ;Dušan Gleich; "InSAR Patch Categorization Using Sparse Coding," vol.14(6), pp.956-960, June 2017. This letter presents sparse coding for interferometric synthetic aperture radar (InSAR) patch categorization. Motivated by the fact that an optimal dual based l1 analysis can achieve better recognition rates, this letter proposes sparse coding with optimal dual-based l1 analysis, which is applied to the amplitude and phase of the InSAR patches. The minimization of cost functions for amplitude and phase was designed and solved differently. The cost function for the amplitude part of InSAR data was modeled using the optimal dual-based l1 analysis, and the minimization of cost function was solved using the forward-backward splitting algorithm. The phase was coded sparsely using the l1 minimization approach and it was solved using the gradient descent algorithm. The experimental results showed that the proposed method outperforms the complex-valued methods for SAR patch categorization and outperforms the bag of visual words method as well.

Aarón Ángel Salas-Sánchez;María Elena López-Martín;Juan Antonio Rodríguez-González;Francisco José Ares-Pena; "Design of Polyimide-Coated Yagi-Uda Antennas for Monitoring the Relative Humidity Level," vol.14(6), pp.961-963, June 2017. Coating an antenna with a hydrophilic polyimide film has been reported to enhance the effects of atmospheric relative humidity on the characteristics of the antenna. In this letter, we designed Yagi-Uda antennas with polyimide-coated dipoles, and we performed a simulation study investigating the influence of atmospheric relative humidity on their resonant frequencies. We conclude that antennas of this type might constitute viable sensors for the measurement of atmospheric relative humidity, and hypothesize that in certain situations such sensors may have advantages over existing alternatives.

Sheng-Ye Jin;Junichi Susaki; "A 3-D Topographic-Relief-Correlated Monte Carlo Radiative Transfer Simulator for Forest Bidirectional Reflectance Estimation," vol.14(6), pp.964-968, June 2017. Understanding the physical processes that affect electromagnetic waves within forests is a key to better analysis of global environmental change. In this letter, we propose a 3-D vector model called the topographic-relief-correlated Monte Carlo (MC) radiative transfer simulator for estimating the bidirectional reflectance factor (BRF) of a forest with complex terrain relief. Unlike existing models, this model takes into account rugged terrain conditions by modeling the ground surface as a bilinear surface interpolated from a digital elevation model. The proposed model is compared with the well-performing MC model FLiES for validation, and good agreement is obtained. Forest BRF estimations for six different terrain relief conditions are derived, and these BRFs have reasonable variation according to ground conditions.

Sangwook Park;Chul Jin Cho;Bonhwa Ku;SangHo Lee;Hanseok Ko; "Compact HF Surface Wave Radar Data Generating Simulator for Ship Detection and Tracking," vol.14(6), pp.969-973, June 2017. Toward a maritime surveillance objective, many ship detection and tracking algorithms have been investigated but are faced with poor performance in practical ocean environments. Compact high-frequency (HF) radar has also faced critical issues due to its long coherent processing interval and varying response from its orthogonal antenna structure. Hence, a simulator based on compact HF radar is proposed in this letter to provide a guideline for effective assessment of ship detection and tracking algorithms while considering these practical issues. To validate the proposed simulator, the simulator generated data has been compared with real data obtained by the compact HF radar sites.

Dong Feng;Daoxiang An;Xiaotao Huang; "Spatial Resolution Analysis for Ultrawideband Bistatic Forward-Looking SAR," vol.14(6), pp.974-978, June 2017. The ultrawideband (UWB) bistatic forward-looking synthetic aperture radar (BFSAR) can realize the high-resolution imaging in the forward direction of the transmitter/receiver, so it has significant applications in both civil and military fields. However, compared to the general narrowband side-looking bistatic synthetic aperture radar systems, the UWB BFSAR systems have larger fractional signal bandwidth and more special acquisition geometry, which will affect the behavior of spatial resolutions. Current methods for spatial resolution analysis of the UWB BFSAR are unsuitable because they do not consider the effects of these two special issues. This letter analyzes the special behavior of spatial resolutions in the UWB BFSAR, and the straightforward gradient method is utilized and extended based on the analysis to calculate the spatial resolutions. The theoretical analysis shows that the range resolution can be calculated by the traditional gradient method directly but the Doppler resolution should be calculated by the extended method. Simulated results verify the correctness of the theoretical analysis and the validity of the proposed method.

Baiyuan Ding;Gongjian Wen;Xiaohong Huang;Conghui Ma;Xiaoliang Yang; "Data Augmentation by Multilevel Reconstruction Using Attributed Scattering Center for SAR Target Recognition," vol.14(6), pp.979-983, June 2017. The quality of synthetic aperture radar (SAR) images and the completeness of the template database are two important factors in template-based SAR automatic target recognition. This letter gives a solution to the two factors by multilevel reconstruction of SAR targets using attributed scattering centers (ASCs). The ASCs of original SAR images are extracted to reconstruct the target's image, which not only reduces the noise and background clutters but also keeps the electromagnetic characteristics of the target. Template database are reconstructed at multilevels to simulate various extents of ASC absence in the extended operation conditions. Therefore, the quality of SAR images as well as the completeness of the template database is augmented. Features are extracted from the augmented SAR images, and the classifier is trained by the augmented database for target recognition. Experimental results on the moving and stationary target acquisition and recognition data set demonstrate the validity of the proposed method.

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J. Figa-Saldaña;K. Scipal;D. Long;M. A. Bourassa;W. Wagner;A. Stoffelen; "Foreword to the Special Issue on “New Challenges and Opportunities in Scatterometry”," vol.10(5), pp.2083-2085, May 2017. The papers in this special section were presented at the 2016 EUMETSAT and ESA conference which explored recent advances in scatterometry and to prepare for the development and exploitation of MetOp-SG scatterometer data.

Ad Stoffelen;Signe Aaboe;Jean-Christophe Calvet;James Cotton;Giovanna De Chiara;Julia Figa Saldaña;Alexis Aurélien Mouche;Marcos Portabella;Klaus Scipal;Wolfgang Wagner; "Scientific Developments and the EPS-SG Scatterometer," vol.10(5), pp.2086-2097, May 2017. The second-generation exploitation of meteorological satellite polar system (EPS-SG) C-band-wavelength scatterometer instrument (called SCA), planned for launch in 2022, has a direct heritage from the successful advanced scatterometer (ASCAT) flown on the current EPS satellites. In addition, SCA will represent three major innovations with respect to ASCAT, namely: 1) Cross polarization and horizontal copolarization; 2) a nominal spatial resolution of 25 km; and 3) 20% greater spatial coverage than ASCAT. The associated expected science and application benefits that led the SCA design are discussed with respect to ocean, land, and sea ice applications for near-real time, climate monitoring, and research purposes. Moreover, an option to implement an ocean Doppler capability to retrieve the ocean motion vector is briefly discussed as well. In conclusion, the SCA instrument innovations are well set to provide timely benefits in all the main application areas of the scatterometer (winds, soil moisture, sea ice) and can be expected to contribute to new and more sophisticated meteorological, oceanographic, land, sea ice, and climate services in the forthcoming SCA era.

Chung-Chi Lin;Wolfgang Lengert;Evert Attema; "Three Generations of C-Band Wind Scatterometer Systems From ERS-1/2 to MetOp/ASCAT, and MetOp Second Generation," vol.10(5), pp.2098-2122, May 2017. The C-band wind scatterometer service to the meteorological community started with the launch of the first European radar satellite ERS-1 on July 17, 1991, followed by its twin-satellite ERS-2 launched on April 21, 1995. The continuity of the ocean surface wind observations after ERS was ensured by the first series of operational polar orbiting meteorological satellites, with MetOp-A and MetOp-B satellites launched on October 19, 2006 and September 17, 2012, respectively. The launch of MetOp-C is planned in 2018 which will ensure a gap-less service until the start of the second generation MetOp mission (MetOp -SG) in 2021/22 for a mission duration of 21 years for the latter. This paper presents an overview of the developments and operations of the ERS and MetOp wind scatterometer systems, concluding with the initial definition of the MetOp-SG scatterometer requirements and design. Technical challenges encountered during the development of the first C-band system (ERS) are briefly described. Lessons learned from the experience of the ERS mission have been reflected in the definition of the MetOp/ASCAT mission, which is currently operated by EUMETSAT. Driven by the progress in the NWP models and increasing demands on providing accurate observations of extreme weather events, such as high intensity storms, the third generation of wind scatterometer on-board MetOp-SG will provide yet higher spatial resolution data with extended wind speed dynamic range. The latter enhancement is enabled by the addition of a cross-polarized backscatter component measurement on top of the VV-polarization capability as implemented in the ERS and MetOp systems.

Ad Stoffelen;Jeroen Adriaan Verspeek;Jur Vogelzang;Anton Verhoef; "The CMOD7 Geophysical Model Function for ASCAT and ERS Wind Retrievals," vol.10(5), pp.2123-2134, May 2017. A new geophysical model function (GMF), called CMOD7, has been developed for intercalibrated ERS (ESCAT) and ASCAT C-band scatterometers. It is valid for their combined incidence angle range and being used to generate wind climate data records. CMOD7 has been developed in several steps as a successor of CMOD5.n. First, CMOD5.n has been adapted to the ASCAT transponder calibration, which is considered more accurate than any ESCAT gain calibration. This results in a linear scaling of the backscatter values. Second, for low winds, there is a clear mismatch between CMOD5.n and the measurements. An independently developed ASCAT C-band GMF, C2013, which performs particularly well for low winds was adopted to improve low winds for the ASCAT incidence angle range. Third, retrievals with CMOD5.n show wind speed probability distribution functions (pdf) that undesirably depend on wind vector cell (WVC) position across the swath. To overcome this effect, a higher order calibration is applied, which matches the wind speed pdfs for all WVCs of ASCAT and ESCAT. The resulting CMOD7 GMF indeed shows overall improved performance on all relevant quality parameters compared to CMOD5.n. It is found that the standard deviations of error for wind speed and wind direction of ASCAT are improved. The same holds for the maximum-likelihood estimates, showing an 8% improved consistency with the local triplet of backscatter measurements. As a consequence, triple collocation with moored buoy and numerical weather prediction winds results in smaller wind vector components and wind direction retrieval errorsim.

Zhixiong Wang;Ad Stoffelen;Franco Fois;Anton Verhoef;Chaofang Zhao;Mingsen Lin;Ge Chen; "SST Dependence of Ku- and C-Band Backscatter Measurements," vol.10(5), pp.2135-2146, May 2017. The normalized radar cross section (NRCS) measured by satellite ocean radar systems is representative of the sea surface roughness at the scale of gravity-capillary waves, which are not only dominated by winds, but also modulated by some secondary factors such as sea surface temperature (SST) and sea surface salinity (SSS). In this paper, the variations of NRCS due to SST changes, depending on scatterometer radar frequency, polarization, and incidence angle, are investigated on the basis of a physics-based radar backscatter model and a dataset of collocated ASCAT C-band and RapidScat Ku-band scatterometer measurements. The study shows that the SST effects are substantial at Ku-band, but rather negligible for C-band NRCS measurements. Furthermore, the SST effects are wind speed dependent and more pronounced in VV polarization and at higher incidence angles. SSS effects, due to dielectric constant, surface tension, and dynamic viscosity variations, on scatterometer winds are limited (within 1%). This study concludes that it is necessary to take SST into account in scatterometer wind retrieval for radar wavelengths smaller than C-band.

Justin Edward Stopa;Alexis Aurélien Mouche;Bertrand Chapron;Fabrice Collard; "Sea State Impacts on Wind Speed Retrievals From C-Band Radars," vol.10(5), pp.2147-2155, May 2017. Scatterometers, a proven technology, provide ocean wind speeds and directions that are essential in operational forecasts, monitoring of the climate, and scientific applications. While the missions and geophysical model functions are performing well, challenges remain. We analyze data from advanced scatterometer (ASCAT) aboard MetOp-A and the advanced synthetic aperture radar (ASAR) aboard Envisat, both of which operate in the C-band, against the in situ buoy wind speeds. We observe large variability in the wind speed residuals. Through analysis of these residuals, we find that they are related to sea state effects and atmospheric stability. The sea state dependence created by low-frequency swells is more pronounced for the lower incidence angles in ASCAT. In ASAR with a fixed angle of 23°, the sea state dominates the wind speed errors and these trends increase with the significant wave height. We observe that wind speeds from ASAR and ASCAT have a close resemblance, which helps us to extrapolate our findings. The synergy between the two technologies can be further exploited to improve wind speed retrievals. Future scatterometer missions, such as the next MetOp, will operate with the wider range of incidence angles (including lower angles) to increase their coverage together, have higher spatial resolution, and obtain measurements closer to the coasts. In these cases, high-resolution SAR data can aide in the understanding of the radar response.

Wenming Lin;Marcos Portabella;Ad Stoffelen;Anton Verhoef; "Toward an Improved Wind Inversion Algorithm for RapidScat," vol.10(5), pp.2156-2164, May 2017. The sea-surface winds from the RapidScat scatterometer (RSCAT) onboard the International Space Station (ISS) have been produced using the Pencil-beam scatterometer Wind Processor (PenWP) since December 2014. An inversion residual or Maximum Likelihood Estimator (MLE-) based quality control (QC) algorithm is included in PenWP to distinguish between goodand poor-quality winds. Generally, the QC-accepted RSCAT winds are in good agreement with both the Advanced Scatterometer (ASCAT) winds and the European Centre for Medium-range Weather Forecasting (ECMWF) model output. In contrast, the QC-rejected winds present an overall positive bias with respect to ASCAT and ECMWF winds, mainly due to the impact of rain. However, it has been recently found that a considerable portion (>5%) of RSCAT QC-rejected contains anomalously low retrieved speeds (ω <; 4 m/s) under medium or high wind conditions (ω > 10 m/s) according to ASCAT/ECMWF. This paper attempts to sort out the cause for these spuriously low winds. A revised MLE inversion with prefiltering the anomalous backscatters is proposed to correct the mentioned inversion issue. The impact of such improved inversion on the retrieved RSCAT winds is evaluated using both the collocated ASCAT and ECMWF winds. The results show that the proposed algorithm improves the wind retrieval of the spuriously low wind cases remarkably, while preserving about 4.7% of the nominal QC-rejected data (0.25% in total).

Frank J. Wentz;Lucrezia Ricciardulli;Ernesto Rodriguez;Bryan W. Stiles;Mark A. Bourassa;David G. Long;Ross N. Hoffman;Ad Stoffelen;Anton Verhoef;Larry W. O'Neill;J. Tomas Farrar;Douglas Vandemark;Alexander G. Fore;Svetla M. Hristova-Veleva;F. Joseph Turk;Robert Gaston;Douglas Tyler; "Evaluating and Extending the Ocean Wind Climate Data Record," vol.10(5), pp.2165-2185, May 2017. Satellite microwave sensors, both active scatterometers and passive radiometers, have been systematically measuring near-surface ocean winds for nearly 40 years, establishing an important legacy in studying and monitoring weather and climate variability. As an aid to such activities, the various wind datasets are being intercalibrated and merged into consistent climate data records (CDRs). The ocean wind CDRs (OW-CDRs) are evaluated by comparisons with ocean buoys and intercomparisons among the different satellite sensors and among the different data providers. Extending the OW-CDR into the future requires exploiting all available datasets, such as OSCAT-2 scheduled to launch in July 2016. Three planned methods of calibrating the OSCAT-2 σo measurements include 1) direct Ku-band σo intercalibration to QuikSCAT and RapidScat; 2) multisensor wind speed intercalibration; and 3) calibration to stable rainforest targets. Unfortunately, RapidScat failed in August 2016 and cannot be used to directly calibrate OSCAT-2. A particular future continuity concern is the absence of scheduled new or continuation radiometer missions capable of measuring wind speed. Specialized model assimilations provide 30-year long high temporal/spatial resolution wind vector grids that composite the satellite wind information from OW-CDRs of multiple satellites viewing the Earth at different local times.

Anton Verhoef;Jur Vogelzang;Jeroen Verspeek;Ad Stoffelen; "Long-Term Scatterometer Wind Climate Data Records," vol.10(5), pp.2186-2194, May 2017. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) produces near-real-time scatterometer ocean vector winds since almost 20 years already. Data from the European remote sensing satellites (ERS-1 and ERS-2 scatterometer), QuikSCAT (SeaWinds), Metop (ASCAT), Oceansat 2 (OSCAT), and RapidScat on the International Space Station have been, or are being, produced. The OSI SAF scatterometer products, produced at the Royal Netherlands Meteorological Institute, provide superior comparison to both buoy and numerical weather prediction (NWP) datasets. Moreover, the wind processing software is publicly available through the EUMETSAT NWP SAF. An increasing amount of users employs scatterometer wind data for climate studies. However, the wind retrieval algorithms have been continuously improved over the years and the currently existing archives of near-real-time data are not always suitable to fulfill the need for homogeneous datasets spanning a longer period of time. Currently, only few validated vector wind climate datasets are available. Therefore, the OSI SAF is reprocessing several offline datasets. This paper is focusing on two climate data records from SeaWinds and ASCAT winds, which together span the period from 1999 to 2014. The data are compared to the NWP model and buoy winds. The stability of the wind characteristics is assessed and an attempt is made to attribute temporal changes to climatological and NWP model changes over time.

Maria Belmonte Rivas;Ad Stoffelen;Jeroen Verspeek;Anton Verhoef;Xavier Neyt;Craig Anderson; "Cone Metrics: A New Tool for the Intercomparison of Scatterometer Records," vol.10(5), pp.2195-2204, May 2017. With an eye on the generation of a long-term climate record of ocean winds, soil moisture, and sea ice extents across the C-band ERS and ASCAT scatterometer spans, a new calibration tool termed cone metrics has been developed. The new method is based on monitoring changes in the location and shape of the surface of maximum density of ocean backscatter measurements, also known as “the wind cone.” The cone metrics technique complements established calibration approaches, such as rain forest and NWP ocean calibration, through the characterization of linear as well as nonlinear beam offsets, the latter via wind cone deformations. Given instrument evolution, proven stability, and the monitoring by transponders, we take ASCAT-A data over 2013 as absolute calibration reference. This paper describes the new method and its application as intercalibration tool in the context of the reprocessing activities for ERS-1 and ERS-2. Cone metrics succeeds at establishing the linear and nonlinear corrections necessary to homogenize the ASCAT and ERS C-band records down to 0.05 dB.

Heather M. Holbach;Mark A. Bourassa; "Platform and Across-Swath Comparison of Vorticity Spectra From QuikSCAT, ASCAT-A, OSCAT, and ASCAT-B Scatterometers," vol.10(5), pp.2205-2213, May 2017. In the last few years, there has been tremendous improvement in the calibration of ocean surface vector winds from scatterometers and polarimetric radiometers. This is the first detailed investigation of across-swath consistency in scatterometer-derived (i.e., QSCAT, ASCAT-A, OSCAT, and ASCAT-B) vorticity (curl of the ocean surface vector winds). Spatial derivatives of the winds fields are very important for atmospheric boundary-layer processes, upper ocean forcing, and deep ocean forcing. Improvements in wind calibration imply improvements in derivatives of these winds; however, it does not imply consistency. This study demonstrates near consistency in across-swath vorticity and near consistency between platforms.

David G. Long; "Comparison of SeaWinds Backscatter Imaging Algorithms," vol.10(5), pp.2214-2231, May 2017. This paper compares the performance and tradeoffs of various backscatter imaging algorithms for the SeaWinds scatterometer when multiple passes over a target are available. Reconstruction methods are compared with conventional gridding algorithms. In particular, the performance and tradeoffs in conventional “drop in the bucket” (DIB) gridding at the intrinsic sensor resolution are compared to high-spatial-resolution imaging algorithms such as fine-resolution DIB and the scatterometer image reconstruction (SIR) that generate enhanced-resolution backscatter images. Various options for each algorithm are explored, including considering both linear and dB computation. The effects of sampling density and reconstruction quality versus time are explored. Both simulated and actual data results are considered. The results demonstrate the effectiveness of high-resolution reconstruction using SIR as well as its limitations and the limitations of DIB and fine-resolution DIB.

Craig Anderson;Julia Figa-Saldaña;John Julian William Wilson;Francesca Ticconi; "Validation and Cross-Validation Methods for ASCAT," vol.10(5), pp.2232-2239, May 2017. The advanced scatterometer (ASCAT) is a fan beam scatterometer carried on board the Metop series of satellites. Its primary objective is to measure ocean backscatter for the retrieval of ocean wind vectors. Two ASCAT instruments (ASCAT-A and ASCAT-B) are operational and are independently calibrated using a number of ground-based transponders. The first seven years of data from ASCAT-A have recently been processed into a climate data record. This paper describes a number of methods for cross-validating the data from the two instruments and for assessing the quality and stability of the climate data record. The methods are based on backscatter from the Amazon rainforest, mean backscatter from the open ocean, comparison of measured and modeled ocean backscatter, and ocean cone metrics. These methods show that the climate data record, which covers the period January 2007 to March 2014, has a very high stability (with trends around 0.005 dB per year), good absolute and relative calibration (better than 0.1 dB), and a good across swath calibration (peak to peak variation of less than 0.1 dB). For operational data covering the period April 2015 to March 2016, the methods indicate that ASCAT-B backscatter is around 0.1-0.2 dB higher than ASCAT-A (depending on which beam is considered). This difference is due to a combination of factors: minor changes in calibration algorithms, a minor change in the behavior of the ASCAT-A internal calibration system, and the strategy used to update calibration files in the processing system.

Mariette Vreugdenhil;Sebastian Hahn;Thomas Melzer;Bernhard Bauer-Marschallinger;Christoph Reimer;Wouter Arnoud Dorigo;Wolfgang Wagner; "Assessing Vegetation Dynamics Over Mainland Australia With Metop ASCAT," vol.10(5), pp.2240-2248, May 2017. Recently, the slope and curvature estimation of the backscatter-incidence angle relationship within the TU Wien retrieval algorithm has been improved. Where previously only climatologies of the slope and curvature parameters were available, i.e., one value for every day of year, slope and curvature are now calculated for every day. This enables the retrieval of time series of vegetation optical depth (τa) from backscatter observations. This study demonstrates the ability to detect interannual variability in vegetation dynamics using τa derived from backscatter provided by the advanced scatterometer on-board Metop-A. τa time series over Australia for the period 2007-2014 are compared to leaf area index (LAI) from SPOT-VEGETATION by calculating the rank correlation coefficient (τa) for original time series and anomalies. High values for τa are found over bare soil and sparse vegetation in central Australia with median τa values of 0.78 and 0.58, respectively. Forests and ephemeral lakes and rivers impact the retrieval of τa, and the negative values for τa are found in these areas. Looking at the annual averages of τa, LAI, and surface soil moisture, significantly high values are found for the anomalously wet years 2010 and 2011. Patterns in the increased τa correspond to regions with increased soil moisture and LAI. Values for τa and LAI are anomalous especially in sparsely vegetated regions, where the flush of grasses increases τa and LAI. Regions with enough precipitation and higher woody vegetation component show a smaller increase in 2010 and 2011. This study demonstrates the skill of τ- , and subsequently of scatterometers, to monitor the vegetation dynamics thanks to the multiincidence angle observation capability.

Susan C. Steele-Dunne;Heather McNairn;Alejandro Monsivais-Huertero;Jasmeet Judge;Pang-Wei Liu;Kostas Papathanassiou; "Radar Remote Sensing of Agricultural Canopies: A Review," vol.10(5), pp.2249-2273, May 2017. Observations from spaceborne radar contain considerable information about vegetation dynamics. The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data. The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain. Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring. Hence, SAR applications, theory, and applications are considered here too. An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications. A description of the current vegetation modeling approaches will be given. A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization. Finally, emerging applications, opportunities and challenges will be identified and discussed. Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.

Kengo Miyaoka;Alexander Gruber;Francesca Ticconi;Sebastian Hahn;Wolfgang Wagner;Julia Figa-Saldaña;Craig Anderson; "Triple Collocation Analysis of Soil Moisture From Metop-A ASCAT and SMOS Against JRA-55 and ERA-Interim," vol.10(5), pp.2274-2284, May 2017. This study investigates the quality of Advanced Scatterometer (ASCAT) surface soil moisture (SSM) retrievals with respect to other SSM products derived from the passive Soil Moisture and Ocean Salinity (SMOS) mission and two reanalysis datasets, i.e., the JRA-55 and the ERA-Interim. In particular, the purposes of this study are to 1) characterize the global error structure of the satellite products, 2) understand the spatiotemporal variability of SSM at global scale, and 3) investigate in which areas the assimilation of satellite data may add value to reanalysis. For these purposes, we applied standard statistical methods as well as triple collocation analysis (TCA) for estimating signal-to-noise ratios (SNR). In line with previous studies, we find large and spatially variable biases between all four datasets, but overall spatiotemporal dynamics as reflected in Hovmöller diagrams agree well. With the exception of arid and semiarid environments, ASCAT performs better than SMOS in terms of both its correlation with the models and the SNR. As a result of TCA, we recognize the potential areas for assimilation of ASCAT data, characterized by a high SNR of the satellite data compared to the models, to be the savanna regions in Africa and Central Asia, southwestern North America, and eastern Australia.

Luca Brocca;Wade T. Crow;Luca Ciabatta;Christian Massari;Patricia de Rosnay;Markus Enenkel;Sebastian Hahn;Giriraj Amarnath;Stefania Camici;Angelica Tarpanelli;Wolfgang Wagner; "A Review of the Applications of ASCAT Soil Moisture Products," vol.10(5), pp.2285-2306, May 2017. Remote sensing of soil moisture has reached a level of good maturity and accuracy for which the retrieved products are ready to use in real-world applications. Due to the importance of soil moisture in the partitioning of the water and energy fluxes between the land surface and the atmosphere, a wide range of applications can benefit from the availability of satellite soil moisture products. Specifically, the Advanced SCATterometer (ASCAT) on board the series of Meteorological Operational (Metop) satellites is providing a near real time (and long-term, 9+ years starting from January 2007) soil moisture product, with a nearly daily (sub-daily after the launch of Metop-B) revisit time and a spatial sampling of 12.5 and 25 km. This study first performs a review of the climatic, meteorological, and hydrological studies that use satellite soil moisture products for a better understanding of the water and energy cycle. Specifically, applications that consider satellite soil moisture product for improving their predictions are analyzed and discussed. Moreover, four real examples are shown in which ASCAT soil moisture observations have been successfully applied toward: 1) numerical weather prediction, 2) rainfall estimation, 3) flood forecasting, and 4) drought monitoring and prediction. Finally, the strengths and limitations of ASCAT soil moisture products and the way forward for fully exploiting these data in real-world applications are discussed.

David G. Long; "Polar Applications of Spaceborne Scatterometers," vol.10(5), pp.2307-2320, May 2017. Wind scatterometers were originally developed for observation of near-surface winds over the ocean. They retrieve wind indirectly by measuring the normalized radar cross section (σ°) of the surface, and estimating the wind via geophysical model function relating σ° to the vector wind. The σ° measurements have proven to be remarkably capable in studies of the polar regions where they can map snow cover; detect the freeze/thaw state of forest, tundra, and ice; map and classify sea ice; and track icebergs. Further, a long time series of scatterometer σ° observations is available to support climate studies. In addition to fundamental scientific research, scatterometer data are operationally used for sea-ice mapping to support navigation. Scatterometers are, thus, invaluable tools for monitoring the polar regions. In this paper, a brief review of some of the polar applications of spaceborne wind scatterometer data is provided. The paper considers both C-band and Ku-band scatterometers, and the relative merits of fan-beam and pencil-beam scatterometers in polar remote sensing are discussed.

Jur Vogelzang;Ad Stoffelen;Richard D. Lindsley;Anton Verhoef;Jeroen Verspeek; "The ASCAT 6.25-km Wind Product," vol.10(5), pp.2321-2331, May 2017. The advanced scatterometer (ASCAT) wind data processor (AWDP) produces ocean surface vector winds from radar measurements by the ASCAT on board the Metop satellites. So far, the ASCAT-coastal product with a grid size of 12.5 km has been the one with the highest resolution. Version 2.4 of AWDP, released May 2016, offers the possibility to process wind data on a 6.25 km grid. In this paper, the true spatial resolution and accuracy of that product is assessed using various methods. The crucial parameter is the radius of the area used to aggregate individual backscatter observations to a wind vector cell (WVC) level. A value of 7.5 km, half of that for ASCAT-coastal, appears to be the best compromise between resolution and accuracy. Spatial responses from multiple radar cross-section measurements are combined to cumulative responses, and show that the ASCAT-6.25 product has a spatial resolution of about 17 km, better than the 28 km found for the ASCAT-coastal product. The accuracy of the ASCAT-6.25 product is estimated using comparison with collocated buoys, triple collocation analysis, and a new method based on spatial variances. These methods show consistently that the ASCAT-6.25 product contains about 0.2 m2/s2 more noise in the wind components than the ASCAT-coastal product, due to the smaller number of individual measurements contributing to the average radar cross section in a WVC. The ASCAT-6.25 product is intended for applications that demand a spatial resolution as high as possible, like the study of dynamical mesoscale phenomena.

Jur Vogelzang;Ad Stoffelen; "ASCAT Ultrahigh-Resolution Wind Products on Optimized Grids," vol.10(5), pp.2332-2339, May 2017. The accuracy and spatial resolution of ultrahigh-resolution wind products derived from full-resolution ASCAT radar cross-section measurements are determined by the size and shape of the aggregation area. Current high-resolution products (ASCAT-coastal and ASCAT-6.25) are defined on a regular swath grid (size 12.5 and 6.25 km, respectively) and use a circular aggregation area (15 and 7.5 km radius resp.). For ASCAT-6.25, such approach leads to poor radar sampling, causing noise in the retrieved winds, and poor beam overlap, causing geophysical errors. More regular radar sampling and improved beam overlap can be obtained by using a grid that is synchronized with respect to the ASCAT mid-beam full-resolution measurements. This results in a new generation of ASCAT wind products. It is shown that a product on a 5.6 km grid size (on average) with optimized radar sampling compares better to buoys than ASCAT-6.25, but at the cost of an irregular grid and slightly degraded spatial resolution.

Jos de Kloe;Ad Stoffelen;Anton Verhoef; "Improved Use of Scatterometer Measurements by Using Stress-Equivalent Reference Winds," vol.10(5), pp.2340-2347, May 2017. Numerical weather prediction (NWP) and buoy ocean surface winds show some systematic differences with satellite scatterometer and radiometer wind measurements, both in statistical results and in local geographical regions. It is possible to rescale these reference winds to remove certain aspects of these systematic differences. Space-borne ocean surface winds actually measure ocean surface roughness, which is related more directly to stress. Air mass density is relevant in the air-sea momentum transfer as captured in the stress vector. Therefore, apart from the already common “neutral wind correction” for atmospheric stratification, also a “mass density wind correction” is investigated here to obtain a better correspondence between satellite stress measurements and buoy or NWP winds. The bicorrected winds are called stress-equivalent winds. Stress-equivalent winds do not strongly depend on the drag formulation used and provide a rather direct standard for comparison and assimilation in user applications. This paper presents details on how this correction is performed and first results that show the benefits of this correction mainly in the extratropical regions.

Sebastian Hahn;Christoph Reimer;Mariette Vreugdenhil;Thomas Melzer;Wolfgang Wagner; "Dynamic Characterization of the Incidence Angle Dependence of Backscatter Using Metop ASCAT," vol.10(5), pp.2348-2359, May 2017. Observing a target from different look and incidence angles is one of the key features of the Advanced Scatterometer (ASCAT) on-board the series of Metop satellites. The incidence angle dependency of backscatter plays an important role in extracting useful information for the retrieval of geophysical parameters. The TU Wien change detection algorithm exploits the multiangle measurement capabilities of ASCAT to retrieve relative surface soil moisture content. In the TU Wien algorithm, the incidence angle dependence of backscatter is characterized with a second-order polynomial and its coefficients are estimated from several years of data due to robustness. Recently, however, it has been shown by Melzer [1], that a kernel smoother (KS) holds promise to characterize the polynomial coefficients on an interannual basis. In this study, we tested the performance and robustness of the KS globally, by comparing the results obtained from ASCAT on-board Metop-A and Metop-B independently. Overall, a good agreement has been found between Metop-A and Metop-B confirming a robust interannual estimation of the incidence angle dependence of backscatter using the KS. However, in two cases, the prevailing conditions on the ground complicated the estimation: areas with very low signal variation and sandy deserts. An analysis of Hovmöller diagrams provided insight into seasonal variations, also revealing small remaining biases between the instruments. The dynamic characterization of the incidence angle dependence of backscatter allows to study the temporal evolution in more detail and, at the same time, moving a step further on the vegetation correction in the TU Wien soil moisture algorithm.

Francesca Ticconi;Craig Anderson;Julia Figa-Saldaña;John Julian William Wilson;Helmut Bauch; "Analysis of Radio Frequency Interference in Metop ASCAT Backscatter Measurements," vol.10(5), pp.2360-2371, May 2017. The advanced scatterometer (ASCAT) is a radar system carried on board the ESA/EUMETSAT METOP series of satellites. It is designed for the purpose of retrieving wind field over oceans. It also provides information on surface soil moisture content and sea ice. Although ASCAT uses a linear frequency modulated pulse with a center frequency of 5.255 GHz (C-band), it is subject to radio frequency interference (RFI). This paper analyses seven years of ASCAT data and shows an increase of the number of noise outliers and an increase of the noise background level over specific land areas. This suggests that the outliers are not a natural occurrence, but are due to RFI from ground-based equipments. As regards the observed increase of the noise background level, it is not straightforward to associate possible RFI sources which could have caused it. However, since the ASCAT has a dynamic range of about 30 dB, the worse measured increase of 1 dB in the noise floor has almost no impact on performance, in particular, on soil moisture retrieval. In addition, the effect of the noise outliers on the estimate of the ASCAT receiver filter shape function used in the processing is also examined and is found to introduce errors of up to 0.4 dB. However, the occurrence of the noise outliers is generally very low, typically two out of 60 000 noise measurements per day, so the impact on the operational use of ASCAT data for wind vector retrieval is limited.

Liling Liu;Xiaolong Dong;Wenming Lin;Jintai Zhu;Di Zhu; "Spatial Resolution and Precision Properties of Scatterometer Reconstruction Algorithms," vol.10(5), pp.2372-2382, May 2017. Various reconstruction methods have been used to enhance the spatial resolution of scatterometer data. Most of the image reconstructions are two-dimensional problems, which combine multiple passes of overlapping data over the temporally homogeneous surface, and thus are only suitable for land and ice applications. This paper attempts to address the one-dimensional reconstruction to enhance the azimuth resolution of scatterometer data using a single pass of observations. Since the range resolution determined by the on-board dechirping technique is generally up to several hundred meters, the one-dimensional reconstruction is adequate for certain near real-time ocean applications, such as the development of coastal scatterometer winds. Three well-known reconstruction algorithms, including additive algebraic reconstruction technique (AART), multiplicative algebraic reconstruction technique (MART), and scatterometer image reconstruction (SIR), are evaluated. The spatial resolution and the reconstruction precision resolved by each algorithm are separately analyzed using the local impulse response and Monte Carlo methods. The dependence of the spatial resolution and the reconstruction precision on a variety of parameters, such as the mean backscatter coefficient and its variance, the beamwidth of spatial response function (SRF), and the SRF function type, is evaluated using a simulation framework. In particular, the tradeoff between the spatial resolution and the reconstruction precision is examined for three algorithms. The results show that SIR offers the quickest convergence and lowest noise.

Gert-Jan Marseille;Ad Stoffelen; "Toward Scatterometer Winds Assimilation in the Mesoscale HARMONIE Model," vol.10(5), pp.2383-2393, May 2017. Data assimilation (DA) experiments have been conducted with the high-resolution limited-area model HirLAM Aladin Regional Mesoscale Operational NWP In Euromed (HARMONIE), which is operational at most weather centers, which are part of the European HirLAM consortium. Recently, the assimilation of scatterometer ocean surface winds was introduced, showing limited forecast skill improvement. Possible explanations are discussed. These include model bias and the time mismatch between observation and analysis time, which introduces nonnegligible correlated errors in a three-dimensional (3-D) variational assimilation system. Also, ignoring the time mismatch increases the innovation, i.e., the observation minus background (model short-term forecast), by about 20% for scatterometer winds. The use of observations as point observations in most DA systems needs reconsideration for mesoscale DA. The introduction of observation operators, taking into account the instrument footprint, would improve the innovation by about 5% for scatterometer winds. Additional directions for improved use of observations in HARMONIE are discussed based on the notice that DA is an inherent deterministic concept. Hence, the selection of the spatial scale for deterministic DA should depend primarily on the 4-D observation coverage rather than the effective model resolution.

Teresa Valkonen;Harald Schyberg;Julia Figa-Saldaña; "Assimilating Advanced Scatterometer Winds in a High-Resolution Limited Area Model Over Northern Europe," vol.10(5), pp.2394-2405, May 2017. Satellite-based scatterometer ocean surface wind measurements have been shown to improve global weather forecasts through data assimilation. However, these scatterometer data are not yet widely assimilated operationally in high-resolution regional models. This paper demonstrates the impact of assimilating Advanced Scatterometer (ASCAT) winds on the analysis and forecasts in observation system experiments using the convection-resolving operational HARMONIE-AROME model system over Northern Europe. At high latitudes, ASCAT provides dense observational data of meteorological phenomena, such as cold-air mesocyclones, particular in this region. Observation errors for ASCAT used in assimilation were found to be consistent with what was found in analysis of observation versus model background statistics, and no spatial error correlations were found on the 50 km separation distances. The largest impact of the assimilation of ASCAT winds was found over the ocean and in the coastal regions. Forecast verification against synoptic observations at coastal stations showed on average improvements for mean sea-level pressure and to some extent for 10-m wind speed on short forecast range. This varied only a little when changing the assimilation settings. Decreasing the data thinning distance from 100 to 50 km further improved forecasts, while shortening the assimilation window from 3 to 1 h did not yield a consistent forecast impact. The observation system experiments have confirmed that scatterometer winds contribute to improved analysis and forecasts in high-resolution regional modeling. This demonstrates general applicability of scatterometer observations for improving weather forecasts at high latitudes.

Saleh Abdalla;Giovanna De Chiara; "Estimating Random Errors of Scatterometer, Altimeter, and Model Wind Speed Data," vol.10(5), pp.2406-2414, May 2017. Scatterometer and altimeter wind data are very important for data assimilation and verification of numerical weather prediction models. Standard deviation of absolute random errors can be estimated using the triple collocation technique. However, error correlations between various wind sources (e.g., due to data assimilation) complicate the error estimation. A method is used to alleviate the impact of error correlations between the scatterometer and the model that assimilates such data. Using twenty-two datasets of triplet composed of Jason-2 altimeter, Metop-A/B scatterometers (ASCAT-A/B, respectively), and ECMWF model analysis and forecasts (1 altimeter × 2 scatterometers × 11 model analysis and forecasts = 22 datasets) covering a period of two years from August 2013 to July 2015, the correlation coefficient between the errors of scatterometers and the model analysis was found to be about 0.33 for those datasets. This correlation reduces with forecast lead time until it almost vanishes at day seven. Altimeter and scatterometer errors are not correlated. The standard deviation of wind speed random errors of Jason-2, ASCAT-A/B, and the IFS analysis are estimated as 0.7, 0.8, and 0.9 m/s, respectively. As expected, there was no difference between ASCAT-A and ASCAT-B results.

Giovanna De Chiara;Massimo Bonavita;Stephen J. English; "Improving the Assimilation of Scatterometer Wind Observations in Global NWP," vol.10(5), pp.2415-2423, May 2017. This study aims at improving the assimilation of scatterometer wind observations in global Numerical Weather Prediction (NWP) model by refining the background quality control and optimizing the observation sampling strategy. To improve the background quality control, different Huber Norm distribution implementations are tested and compared against the current “Gaussian plus flat” distribution. Sensitivity experiments show that the usage of the Huber Norm distribution improves the analysis and forecasts. The benefit is mainly seen in the lower model levels in the tropics and extra-tropical Southern Hemisphere. The optimal wind sampling is investigated by testing several combinations of thinning scheme and observation error standard deviation. The impact is demonstrated with a large sample and illustrated by a case study. The case study shows the impact of different settings on the analysis and forecast of a tropical cyclone. A revised wind sampling setting, where four times more observations and a higher observation error than the current operational one are used, showed slightly positive impact on the European Centre for Medium-Range Weather Forecasts (ECMWF) global NWP analyses and forecasts.

* "Become a published author in 4 to 6 weeks," vol.10(5), pp.2424-2424, May 2017.* Advertisement, IEEE.

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Wenzhi Liao;Jocelyn Chanussot;Mauro Dalla Mura;Xin Huang;Rik Bellens;Sidharta Gautama;Wilfried Philips; "Taking Optimal Advantage of Fine Spatial Resolution: Promoting partial image reconstruction for the morphological analysis of very-high-resolution images," vol.5(2), pp.8-28, June 2017. Diverse sensor technologies have allowed us to measure different aspects of objects on Earth's surface [such as spectral characteristics in hyperspectral images and height in light detection and ranging (LiDAR) data] with increasing spectral and spatial resolutions. Remote-sensing images of very high geometrical resolution can provide a precise and detailed representation of the monitored scene. Thus, the spatial information is fundamental for many applications. Morphological profiles (MPs) and attribute profiles (APs) have been widely used to model the spatial information of very-high-resolution (VHR) remote-sensing images. MPs are obtained by computing a sequence of morphological operators based on geodesic reconstruction. However, both morphological operators based on geodesic reconstruction and attribute filters (AFs) are connected filters and, hence, suffer the problem of leakage (i.e., regions related to different structures in the image that happen to be connected by spurious links are considered as a single object). Objects expected to disappear at a given stage remain present when they connect with other objects in the image. Consequently, the attributes of small objects are mixed with their larger connected objects, leading to poor performances on postapplications (e.g., classification).

Naoto Yokoya;Claas Grohnfeldt;Jocelyn Chanussot; "Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature," vol.5(2), pp.29-56, June 2017. In recent years, enormous efforts have been made to design image-processing algorithms to enhance the spatial resolution of hyperspectral (HS) imagery. One of the most commonly addressed problems is the fusion of HS data with higher spatial resolution multispectral (MS) data. Various techniques have been proposed to solve this data-fusion problem based on different theories, including component substitution (CS), multiresolution analysis (MRA), spectral unmixing, and Bayesian probability. This article presents a comparative review of those HS-MS fusion techniques with extensive experiments. Ten state-of-the-art HS-MS fusion methods are compared by assessing their fusion performance both quantitatively and visually. Eight data sets featuring different geographical and sensor characteristics are used in the experiments to evaluate the generalizability and versatility of the fusion algorithms. To maximize the fairness and transparency of this comparison, publicly available source codes are used, and parameters are individually tuned for maximum performance.

Jie-Bang Yan;Sivaprasad Gogineni;Fernando Rodriguez-Morales;Daniel Gomez-Garcia;John Paden;JiLu Li;Carlton J. Leuschen;David A Braaten;Jacqueline A Richter-Menge;Sinead Louise Farrell;John Brozena;Richard D. Hale; "Airborne Measurements of Snow Thickness: Using ultrawide-band frequency-modulated-continuous-wave radars," vol.5(2), pp.57-76, June 2017. Frequency-modulated-continuous-wave (FM-CW) radar has been used extensively for the airborne measurement of snow thickness over sea ice and the mapping of annual accumulation over land ice. In contrast to conventional in situ measurements, FM-CW radar, when operated onboard an airborne platform, can be a useful tool for widearea surveys of snow deposition. Since the early 2000s, the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas (KU) has designed, developed, and deployed airborne ultrawide-band (UWB) FM-CW radars, called Snow Radars, on National Science Foundation (NSF)-, NASA-, Naval Research Laboratory (NRL)-, and Alfred Wegener Institute (AWI)-provided aircraft in both Arctic and Antarctic regions and generated a large amount of snow data products. In addition to the significant standalone value of the snow-thickness measurements, these data are being used in estimating Arctic sea ice thickness, which is a key variable in the study of atmosphere-ocean-ice interactions. This article provides a review of snow remote sensing techniques with airborne FM-CW radars to document the operating principle, design, and evolution of CReSIS' UWB FM-CW radars and discuss and promote understanding of the advantages and limitations associated with these systems.

Jitao Yang;Guoqing Li; "The China GEO Data Center: Bringing order to open Earth-observation data," vol.5(2), pp.77-85, June 2017. Numerous remote-sensing devices observe the Earth every day, generating large amounts of remotesensing images and metadata. The extensive Earth obser vation (EO) data made available on the Internet by different EO agencies in multiple countries pose challenges for integration, storage, access, and collaborative applications. The capacity to explore and utilize all these heterogeneous EO data from across the world is very limited. In this article, we introduce the China Group on Earth Observations (GEO) Data Center, which is devoted to the efficient integration of EO data on the Internet and opening China's EO data to the world. We describe the infrastructure of the China GEO Data Center and discuss the design and implementation of the China Global Earth Observation System of Systems (GEOSS). We demonstrate the use of the GEO discovery and access broker (DAB) to harvest the large amounts of open EO data brokered by the GEO from different agencies around the world. We describe a data model we have designed to express the relations among EO data and capture the implicit semantic knowledge in EO data through rules (a model implemented using the Apache Jena framework). Finally, we describe the mechanisms for opening China's EO data to the world.

Pierre-Philippe Mathieu;Maurice Borgeaud;Yves-Louis Desnos;Michael Rast;Carsten Brockmann;Linda See;Ravi Kapur;Miguel Mahecha;Ursula Benz;Steffen Fritz; "The ESA's Earth Observation Open Science Program [Space Agencies[Name:_blank]]," vol.5(2), pp.86-96, June 2017. The world of Earth observation (EO) data is rapidly changing, driven by exponential advances in sensor and digital technologies. Recent decades have seen the development of extraordinary new ways of collecting, storing, manipulating, and transmitting data that are radically transforming the way we conduct and organize science. This convergence of technologies creates new challenges for EO scientists and data and software providers to fully exploit large amounts of multivariate data from diverse sources. At the same time, these technological trends also generate huge opportunities to better understand our planet and turn big data into new types of information services. This article briefly describes some of the elements of the European Space Agency's (ESA) EO Open Science program, which aims to enable the digital transformation of the EO community and make the most of the large, complex, and diverse data delivered by the new generation of EO missions, such as the Copernicus Sentinels.

Fawwaz Ulaby; "Remote Sensing Code Library [Software and Data Sets[Name:_blank]]," vol.5(2), pp.95-96, June 2017. Forward progress in remote sensing science and technology relies on the free exchange of results and ideas as well as the dissemination of remotely sensed data acquired by ground-based, airborne, and spaceborne sensors. An important ingredient is the family of computer codes and algorithms used by scientists and engineers to analyze data, model and calibrate sensors, and associate sensor output with the physical parameters of the observed scenes. Scientific journals and meetings—such as IEEE Transactions on Geoscience and Remote Sensing and the IEEE International Geoscience and Remote Sensing symposia—play an important role in facilitating the exchange of scientific information, but their domains do not encompass computer codes. For the most part, remote sensing computer codes remain hidden from public view. The Remote Sensing Code Library (RSCL) is an IEEE Geoscience and Remote Sensing Society initiative to establish a large family of remote sensing computer codes—contributed by members of the remote sensing community and their host institutions—for use by other members of the community not only to verify and extend published results but to also reduce the duplication of time and effort invested in the development of these codes.

Animesh Maitra; "A Profile of Remote-Sensing Activities in the Kolkata Chapter 2015-2016 [Chapters[Name:_blank]]," vol.5(2), pp.97-100, June 2017. Presents GRSS events from the Kolkata Chapter.

Dinesh Sathyamoorthy;Biwajeet Pradhan;Koo Voon Chet;Hean Teik Chuah; "IEEE GRSS Malaysia Chapter [Chapters[Name:_blank]]," vol.5(2), pp.100-102, June 2017. Presents GRSS chapter events from Malaysia.

Lori Mann Bruce; "Promoting the Success of Women in Engineering Through Affinity Groups [Women in GRS[Name:_blank]]," vol.5(2), pp.103-105, June 2017. Presents information on the Women in Engineering (WIE) Affinity Groups whose purpose is to promote and encourage women engineers in their careers.

Linda Hayden; "Ground Station Operations Training Hosted by Eastern North Carolina Chapter Chapter [Education[Name:_blank]]," vol.5(2), pp.106-106, June 2017. Presents GRSS chapter events from the North Carolina Chapter.

* "GRSS Members Elevated to Senior Member in February 2017 [GRSS Member Highlights[Name:_blank]]," vol.5(2), pp.107-107, June 2017.* Lists the GRSS members who were elevated to the status of Senior Member.

* "IEEE Geoscience and Remote Sensing Society?s 2016 Best Reviewers [GRSS Member Highlights[Name:_blank]]," vol.5(2), pp.107-108, June 2017.* Lists the best reviewers of GRSS society publications in 2016.

* "[Calendar[Name:_blank]]," vol.5(2), pp.C3-C3, June 2017.* Presents upcoming events and meetings of interest to GRSS society members.

* "CPGIS 2017," vol.5(2), pp.C3-C3, June 2017.* Presents information on the CPGIS 2017 conference.

Topic revision: r6 - 22 May 2015, AndreaVaccari
 
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