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Last Update: 2012.02.07

VIVA: Automated Analysis of InSAR images

Project Info


Sinkhole Detection, Landslide and Bridge Monitoring for Transportation Infrastructure by Automated Analysis of Interferometric Synthetic Aperture Radar Imagery


Develop image analysis techniques to detect bridge settlements, landslide occurrences and sinkhole formation using space-borne interferometric synthetic aperture radar images stacks.

Funded by:

Research Innovative Technology Administration - U.S. Department of Transportation

VIVA Team Members:

Scott Acton, Qian Sang, Michael Stuecheli, Andrea Vaccari


Early detection of potential transportation hazards to improve safety and reduce maintenance costs.


This project, sponsored by the U.S. Department of Transportation Research and Innovative Technology Administration, investigates the applicability of new commercial remote sensing technology to three transportation-related problems. First, the project addresses the high-risk, high-reward problem of early detection of sinkholes. Then, the same satellite based technology will be used to monitor landslides and bridge settlement. The proposed solutions are fueled by the combination of new millimeter-radar (InSAR) and novel image analysis algorithms developed at the University of Virginia (UVA). A partnership consisting of UVA, the Virginia Transportation Research Council (VTRC), and TRE Canada (the supplier of the data), has been forged to tackle these important transportation application.

Fig.1 Sinkhole development

In the project, the feasibility of using the remotely sensed data to detect and monitor sinkholes, settling bridges and landslides will be assessed. Automated methods for analyzing the images will be developed and tested in a selected region of Virginia within the I-81 Interstate corridor. The end product of the research will be a suite of software tools that can be used by the state departments of transportation across the U.S. to automatically detect and monitor potential sinkholes, bridge settlement and landslide movement from satellite imagery. It is anticipated that the automated processing tools, in combination with the newly available commercial remote sensing data, will lead to multi-million dollar cost savings in the highway repairs, significant reduction in highway closures and enhanced safety of the traveling public.

Fig.2 Bridge settlement detection



  • Detection of geophysical features in InSAR point cloud data sets using spatiotemporal models

    Abstract - In this article, we introduce an approach for detecting evolving geophysical features within interferometric synthetic aperture radar (InSAR)-derived point cloud data sets. This approach is based on the availability of models describing both spatial and temporal behaviours of the geophysical features of interest. The model parameters are used to generate a multidimensional space that is then scanned with a user-defined resolution. For each point in the parameter space, a spatiotemporal template is reconstructed from the original model. This template is then used to scan the point cloud data set for regions matching the spatiotemporal behaviour.
    We also introduce a proportional measure where the residual for each point in the data set is compared to both the data and the template to provide a scale invariant measure of the behavioural matching. The matching is evaluated for every point in the parameter over a region of influence determined by the parameters. The resulting multidimensional space is then collapsed onto geographical coordinates to produce an overlay map identifying regions whose spatiotemporal behaviour matches the feature of interest.
    We tailored our approach to the detection of subsidence behaviour, indicative of the development of sinkholes, modelled as Gaussian with amplitude linearly increasing with time. We verified the validity of our model using both synthetic and actual InSAR data sets. The latter was obtained by processing imagery of a region near Wink, Texas, containing ground truth sinkhole data.
    We applied this framework to a 40 km x 40 km area of interest located in western Virginia and performed ground validation on a subset of the identified regions. The results show good agreement between the locations detected by our algorithm and the evidence of subsidence observed during the ground validation campaign.

    Vaccari, A.; Stuecheli, M.; Bruckno, B.; Hoppe, E.; Acton, S.T.; , "Detection of geophysical features in InSAR point cloud data sets using spatiotemporal models," International Journal of Remote Sensing , vol.34, no.22, pp.8215-8234. doi: 10.1080/01431161.2013.833357

  • Spatiotemporal Gaussian feature detection in sparsely sampled data with application to InSAR

    Abstract - Point cloud data present a broad swath of intriguing problems in signal processing. Namely, the data may be sparse, may be non-uniformly sampled in space and time, and cannot be processed directly by way of conventional techniques such as convolutional filters. This paper addresses such data under the application umbrella of remote sensing. Specifically, we examine the potential of interferometric synthetic aperture radar for detecting geohazards that affect transportation. Using sparsely distributed coherent scatterers on the ground, our algorithms attempt to locate events in process such as sinkholes in the vicinity of highways. Theoretically, the problem boils down to the detection of Gaussian-shaped changes that evolve predictably in space and time. The solution to the detection problem involves two basic approaches, one grounded in pattern matching and the other in statistical signal processing. Essentially, the spatiotemporal pattern matching extends a Hough-like voting algorithm to a method that penalizes deviation from the known model in space and time. For confirmation of geohazard location, we can exploit a fixed-time analysis of the distribution of subsidence from the point cloud data by way of computing mutual information. Results show that the detection and screening strategies conform to geological evidence.

    Vaccari, A.; Acton, S.T.; , "Spatiotemporal Gaussian feature detection in sparsely sampled data with application to InSAR," Proc. SPIE 8746, Algorithms for Synthetic Aperture Radar Imagery XX, 87460U (June 3, 2013) doi: 10.1117/12.2020669

  • Graph Cut Segmentation of Sparsely Sampled Images with Application to InSAR-measured Changes in Elevation

    Abstract - In this paper, we outline an algorithm for the automatic segmentation of sparse data in order to detect possible terrain-deformation phenomena. Segmentation is accomplished through a graph cut technique. In the graph structure, for each edge, we derive a unique energy by combining multiple independent energies tailored toward accurately locating the boundaries of spot-like, subsiding regions. We then find the series of cuts with minimum total energy and fit splines to these cuts for smooth segment boundaries. The segmentation approach is applied to the problem of localizing sinkholes in karst regions. Test results indicate efficacy for a sufficient density of InSAR features.

    Stuecheli, M.; Vaccari, A.; Acton, S.T.; , "Graph cut segmentation of sparsely sampled images with application to InSAR-measured changes in elevation," Image Analysis and Interpretation (SSIAI), 2012 IEEE Southwest Symposium on , vol., no., pp.149-152, 22-24 April 2012. doi: 10.1109/SSIAI.2012.6202475


  • Transportation Research Board - 92nd Annual Meeting - January 13-17, 2013

    Workshop: 135 - Sensing Technologies for Transportation Applications

    Acton, S.T.; "Model for Sinkhole Detection by InSAR" (P13-6215) [.pdf 2MB]

  • Transportation Research Board - 91st Annual Meeting - January 22-26, 2012

    Workshop: 184 - Sensing Technologies for Transportation Applications

    Acton, S.T.; "Investigating Interferometric Synthetic Aperture Radar for Transporation Infrastructure Monitoring" (P12-6176) [.pdf 2MB]


  • Surface Reconstruction [.zip]

    Matlab implementation of mosaicked, thin-plate smoothing spline surface reconstruction. Example included.

  • GIS Export [.zip]

    Matlab routines which export polygon risk information into Google Earth and ArcGIS. ArcGIS export requires the mapping toolbox.

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