Multiresolution SAR data fusion for unsupervised change detection

Satellite synthetic aperture radar (SAR) systems currently offer both very high resolutions and multiresolution acquisition capability, thus presenting a great potential for environmental monitoring and damage assessment applications. In this framework, change detection methods play a central role. In this paper, a novel unsupervised change detection method is proposed for multitemporal SAR images acquired at multiple resolutions. The method combines Markov random field modeling, line processes, linear mixtures, Bayesian estimation, generalized Gaussian distributions, and graph cuts with the aim of fusing the available multiresolution information to generate a change map at the finest of the observed resolutions. The proposed method is experimentally validated with multitemporal COSMO-SkyMed stripmap and polarimetric data.