Robust Low-rank Change Detection for SAR Image Time Series

Ammar Mian (1, 2) , Arnaud Breloy (3) , Guillaume Ginolhac (1, 2) , Jean-Philippe Ovarlez (1, 4)

1 SONDRA - Sondra, CentraleSupélec, Université Paris-Saclay (COmUE)
2 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance
3 LEME - Laboratoire Energétique Mécanique Electromagnétisme
4 DEMR, ONERA, Université Paris Saclay (COmUE) [Palaiseau]

This paper considers the problem of detecting changes in mul-tivariate 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 .

Mots clés: CHANGE DETECTION; SYNTHETIC APERTURE RADAR; LOW RANK; COMPOUND GAUSSIAN

Mots clés: DETECTION DE CHANGEMENT; RADAR A OUVERTURE SYNTHETIQUE; RANG FAIBLE; GAUSSIEN COMPOSE

Domaines: Sciences de l'ingénieur [physics]; Physique [physics]

Ammar Mian, Arnaud Breloy, Guillaume Ginolhac, Jean-Philippe Ovarlez. Robust Low-rank Change Detection for SAR Image Time Series. IGARSS 2019, Jul 2019, YOKOHAMA, Japan. ⟨hal-02345330v1⟩