Robust unsupervised nonparametric change detection of SAR images

Abstract

This paper presents an unsupervised nonparametric method for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method relies on a novel feature capable of capturing the structural changes between the two images and discarding almost completely the statistical changes due to speckle patterns or co-registration inaccuracies. This feature utilizes the scatterplots of the amplitude levels in the two SAR images and applies a fast version of the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. The value of the probability density function (PDF) is translated to a value of conditional information and given to all image pixels originating such modes. Experimental results have been carried out with simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation. The proposed feature exhibits significantly better discrimination capability than both the classical log-ratio (LR) and is particularly robust if applied to SAR images having different processing and/or acquisition angles.