Change Detection of Multilook Polarimetric SAR Images Using Heterogeneous Clutter Models

In this paper, we present a novel unsupervised change detection scheme for multilook polarimetric synthetic aperture radar (PolSAR) images using heterogeneous clutter models. First, a multilook product model is introduced to describe the heterogeneous clutter for multilook PolSAR data, and a corresponding covariance matrix estimation method is derived. Based on this model, a new similarity measure is proposed to quantify the degree of evolution between the statistical characteristics of multitemporal fully PolSAR images. Compared with the classical similarity measure of Wishart distribution, both the structure of the covariance matrix and the power information of PolSAR clutter are considered in the proposed similarity measure. A Kittler and Illingworth (K&I) minimum-error threshold segmentation method is applied to extract the changed areas. Both the simulated PolSAR data set and two three-look Radarsat-2 fully polarimetric images of Suzhou, China, acquired on April 9, 2009 and June 15, 2010, are used for our experiment. The results demonstrate that the proposed change detection method can give a much higher detection rate and a lower false alarm rate than the method using the Wishart similarity measure.