Region-Based Change Detection for Polarimetric SAR Images Using Wishart Mixture Models

The change detection of polarimetric synthetic aperture radar (PolSAR) images is a longstanding and challenging task, not only because of the speckle issue but also due to the complex texture, which generally appears highly heterogeneous. There are two widely used approaches for the change detection of PolSAR images: one is the post classification comparison algorithm, and the other is the directly unsupervised change detection algorithm. In this paper, we focus on the latter and propose a region-based change detection method for PolSAR images by means of Wishart mixture models (WMMs). The WMMs fit the distribution of PolSAR images with less errors both in the homogeneous and the extremely heterogeneous area. More precisely, two PolSAR images are first segmented into compact local regions using the customized simple-linear-iterative-clustering algorithm, while the WMMs are used to model each local region. To generate a difference map, statistical distribution differences measured by information theoretic divergence are then computed for corresponding local region pairs. The Cauchy-Schwarz divergence is adopted as its analytic expression can be derived for WMMs. Finally, the change detection results are obtained by the Kittler-Illingworth thresholding method with Markov random field-based smoothing. The proposed scheme is tested on different PolSAR data sets. Qualitative and quantitative evaluations show its superior performance comparing to the traditional pixel-level approach.