SAR image change detection using distance between distributions of classes

The problem of detecting abrupt changes in a set of synthetic aperture radar (SAR) images is carried out by comparing the results of segmentation between images with different modalities acquired before and after a disaster. Individual segmentations are not considered by themselves since they yield surface map characterization while the goal is to detect a temporal evolution of soil characteristics. Hence, we propose to use the modification of class statistics in the images in order to characterize potential changes and to prevent from false alarms that may be induced by the specific modality of each SAR acquisition. The change detection process is divided in two steps; 1) segmentation of the observations in order to have an estimation of the marginal probability distribution function (pdf) of each class; 2) comparison of the pdf from different images to detect changes by means of evidential and paradoxical reasoning. This two-stages process has been applied on Radarsat images (F2 and F5) of the Nyiragongo volcano, DR Congo, erupted on January 2002. The results obtained outperform simple strategies based on image differencing/ratioing