A Generalized Gamma Copula Model for High Resolution Polarimetric SAR Change Detection

In this paper, we describe a new approach to non-coherent change detection for high resolution polarimetric synthetic aperture radar (polSAR) exploitation. In the high resolution setting, the reduced size of a resolution cell diminishes the applicability of central limit theorem arguments that lead to the traditional Gaussian backscatter models that underpin existing polSAR change detection algorithms. To mitigate this, we introduce a new model for polSAR data that combines generalized Gamma (GG) distributed marginals within a copula framework to capture the correlation dependency between multiple polSAR channels. Using the GG-copula model, a generalized likelihood ratio test (GLRT) is derived for detecting changes within high resolution polSAR imagery. Examples using measured data demonstrate the non-Gaussian nature of high resolution polSAR data and quantify a performance improvement when using the proposed GG-copula change detection framework.