Polarimetric Synthetic-Aperture-Radar Change-Type Classification With a Hyperparameter-Free Open-Set Classifier

Mark W. Koch, R. Derek West, Robert Riley, Tu-Thach Quach; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 1239-1246

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. Coherent change detection (CCD) in SAR can identify minute changes such as vehicle tracks that occur between images taken at different times. From polarimetric SAR capabilities, researchers have developed decompositions that allow one to automatically classify the scattering type in a single polarimetric SAR (PolSAR) image set. We extend that work to CCD in PolSAR images to identify the type change. Such as change caused by no return regions, trees, or ground. This work could then be used as a preprocessor for algorithms to automatically detect tracks.

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[bibtex]
@InProceedings{Koch_2018_CVPR_Workshops,
author = {Koch, Mark W. and Derek West, R. and Riley, Robert and Quach, Tu-Thach},
title = {Polarimetric Synthetic-Aperture-Radar Change-Type Classification With a Hyperparameter-Free Open-Set Classifier},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}