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2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
ISSN: 2160-7516
ISBN: 978-1-4673-6758-5
pp: 151-160
Mark W. Koch , Sandia National Laboratories, Albuquerque, NM 87185-1163, United States
Mary M. Moya , Sandia National Laboratories, Albuquerque, NM 87185-1163, United States
Jim G. Chow , Sandia National Laboratories, Albuquerque, NM 87185-1163, United States
Jeremy Goold , Sandia National Laboratories, Albuquerque, NM 87185-1163, United States
Rebecca Malinas , Sandia National Laboratories, Albuquerque, NM 87185-1163, United States
ABSTRACT
Synthetic aperture radar (SAR) is a remote sensing technology that can truly operate 24/7. It's an all-weather system that can operate at any time except in the most extreme conditions. By making multiple passes over a wide area, a SAR can provide surveillance over a long time period. For high level processing it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we concentrate on automatic road segmentation. This not only serves as a surrogate for finding other static features, but road detection in of itself is important for aligning SAR images with other data sources. In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. We also show how a modified Kolmogorov-Smirnov test can be used to model the static features even when the independent observation assumption is violated.
INDEX TERMS
Roads, Synthetic aperture radar, Image segmentation, Speckle, Optimization, Image resolution, Image edge detection
CITATION
Mark W. Koch, Mary M. Moya, Jim G. Chow, Jeremy Goold, Rebecca Malinas, "Road segmentation using multipass single-pol synthetic aperture radar imagery", 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), vol. 00, no. , pp. 151-160, 2015, doi:10.1109/CVPRW.2015.7301309
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