Road Segmentation Using Multipass Single-Pol Synthetic Aperture Radar Imagery

Mark W. Koch, Mary M. Moya, James G. Chow, Jeremy Goold, Rebecca Malinas; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015, pp. 151-160

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.

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[bibtex]
@InProceedings{Koch_2015_CVPR_Workshops,
author = {Koch, Mark W. and Moya, Mary M. and Chow, James G. and Goold, Jeremy and Malinas, Rebecca},
title = {Road Segmentation Using Multipass Single-Pol Synthetic Aperture Radar Imagery},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2015}
}