Change detection on SAR images by using a parametric estimation of the Kullback-Leibler divergence

Presents a method for performing change detection using a pair of SAR images acquired at different dates. The main difficulty with SAR images is the presence of speckle noise which may produce noisy change images if they are acquired with slightly different angles. The technique proposed in the present paper uses a parametric estimation of the probability distributions locally in each image as a characterization of the surfaces. The change is measured as a distance between these probability laws. The dissimilarity measure between the statistical distributions used here is a symmetric version of the Kullback-Leibler divergence.