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

Ammar Mian, Antoine Collas, Arnaud Breloy, Guillaume Ginolhac, Jean-Philippe Ovarlez

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
42 Downloads (Pure)


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.

Original languageEnglish
Article number9107411
Pages (from-to)3545-3556
Number of pages12
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Early online date2020
Publication statusPublished - 2020
MoE publication typeA1 Journal article-refereed


  • Change detection
  • Synthethic Aperture Radar
  • Time Series
  • Covariance matrix
  • Low-rank
  • Compound-Gaussian


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