Robust Low-Rank Change Detection for Multivariate SAR Image Time Series

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; pp. 3545 - 3556
• Main Authors: Mian, Ammar, Collas, Antoine, Breloy, Arnaud, Ginolhac, Guillaume, Ovarlez, Jean-Philippe
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Change detection; compound Gaussian (CG); Compounds; Covariance matrices; Covariance matrix; Detection; Detectors; Dimensions; Image detection; Likelihood ratio; low-rank (LR); Matrix methods; Multivariate analysis; Normal distribution; Radar imaging; Radar polarimetry; Robustness; SAR (radar); Synthetic aperture radar; synthetic aperture radar (SAR); Testing; Time series; Time series analysis
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.2999615

This article derives a new change detector for multivariate synthetic aperture radar (SAR) image time series (ITS). Classical statistical change detection 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 the relevant signals usually lie in a low-dimensional subspace (low-rank structure). These two issues are tackled by proposing a new generalized likelihood ratio test based on a robust (compound Gaussian) low-rank (structured covariance matrix) model. The interest of the proposed detector is assessed on two SAR-ITS set from UAVSAR.