Nonparametric Change Detection in Multitemporal SAR Images Based on Mean-Shift Clustering

A nonparametric method for unsupervised change detection in multipass synthetic aperture radar (SAR) imagery is described. The method relies on a novel feature capturing the structural change between two SAR images and is robust to the statistical change that may be originated by speckle and coregistration inaccuracies. The proposed method starts from the scatterplot of the amplitude levels in the two images and applies the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. If we assume that the two images have been preliminarily coregistered and calibrated on one another, then all the modes lying outside the main diagonal correspond to the structural changes across the two observations. The value of the probability density function (PDF) in any of the off-diagonal modes found by the MS algorithm is translated into a value of conditional information. This value is assigned to all image pixels generating the corresponding cluster in the scatterplot. Thus, a feature is obtained on a per-pixel basis. Experimental results on simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation show that the proposed feature exhibits significantly better discrimination capability than the classical log-ratio (LR). Advantages over a preliminary version of the method without MS regularization and over another nonparametric method based on Kullback-Leibler divergence are also demonstrated. The method is robust when it is applied to SAR images with different acquisition angles, whose effects are deemphasized compared to the actual scene changes.