Change detection in multisensor SAR images using bivariate gamma distributions

Florent Chatelain (1) , Jean-Yves Tourneret (1) , Jordi Inglada

1 IRIT - Institut de recherche en informatique de Toulouse

This paper studies a family of distributions constructed from multivariate gamma distributions to model the statistical properties of multisensor synthetic aperture radar (SAR) images. These distributions referred to as multisensor multivariate gamma distributions (MuMGDs) are potentially interesting for detecting changes in SAR images acquired by different sensors having different numbers of looks. The first part of the paper compares different estimators for the parameters of MuMGDs. These estimators are based on the maximum likelihood principle, the method of inference function for margins and the method of moments. The second part of the paper studies change detection algorithms based on the estimated correlation coefficient of MuMGDs. Simulation results conducted on synthetic and real data illustrate the performance of these change detectors.

Mots clés: Change detection; Bivariate gamma distributions

Domaines: Traitement du signal et de l'image [eess.SP]; Synthèse d'image et réalité virtuelle [cs.GR]

Florent Chatelain, Jean-Yves Tourneret, Jordi Inglada. Change detection in multisensor SAR images using bivariate gamma distributions. IEEE Transactions on Image Processing, 2008, 1 (3), pp.249-258. ⟨10.1109/TIP.2008.916047⟩. ⟨hal-03579543⟩