Road Segmentation Using Multipass Single-Pol Synthetic Aperture Radar Imagery
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.
Cite
Text
Koch et al. "Road Segmentation Using Multipass Single-Pol Synthetic Aperture Radar Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301309Markdown
[Koch et al. "Road Segmentation Using Multipass Single-Pol Synthetic Aperture Radar Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/koch2015cvprw-road/) doi:10.1109/CVPRW.2015.7301309BibTeX
@inproceedings{koch2015cvprw-road,
title = {{Road Segmentation Using Multipass Single-Pol Synthetic Aperture Radar Imagery}},
author = {Koch, Mark W. and Moya, Mary M. and Chow, Jim G. and Goold, Jeremy and Malinas, Rebecca},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2015},
pages = {151-160},
doi = {10.1109/CVPRW.2015.7301309},
url = {https://mlanthology.org/cvprw/2015/koch2015cvprw-road/}
}