Robust Low-Rank Change Detection for Sar Image Time Series

This paper considers the problem of detecting changes in multivariate Synthetic Aperture Radar image time series. Classical methodologies based on covariance matrix analysis are usually built upon the Gaussian assumption, as well as an unstructured signal model. Both of these hypotheses may be inacc...

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Bibliographic Details
Main Authors: Mian, A., Breloy, A., Ginolhac, G., Ovarlez, J.-P.
Format: Conference Proceeding
Language:English
Subjects:
Change detection
Compound Gaussian
Compounds
Covariance matrices
Detectors
Image resolution
Low Rank
Microsoft Windows
Radar polarimetry
Synthetic aperture radar
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Summary:This paper considers the problem of detecting changes in multivariate Synthetic Aperture Radar image time series. Classical methodologies based on covariance matrix analysis are usually built upon the Gaussian assumption, as well as an unstructured signal model. Both of these hypotheses may be inaccurate for high-dimension/resolution images, where the noise can be heterogeneous (non-Gaussian) and where all channels are not always informative (low-rank structure). In this paper, we tackle these two issues by proposing a new detector assuming a robust low-rank model. Analysis of the proposed method on a UAVSAR dataset shows promising results.
ISSN:2153-7003
DOI:10.1109/IGARSS.2019.8898744