Divergence-based tests for the bivariate gamma distribution applied to polarimetric synthetic aperture radar.

The use of polarimetric synthetic aperture radar (PolSAR) is one of the most successful tools for solving remote sensing problems. The multidimensional speckle noise encountered in the acquisition of these images is the main challenge for PolSAR users. Therefore, tailored processing of PolSAR images is required, especially for the use of hypothesis testing in change detection. In this paper, we use McKay's bivariate gamma distribution (MBG) to describe a joint distribution resulting from two components of the total scattering power image (SPAN). We derive closed form expressions for the MBG Kullback–Leibler and Rényi divergences between SPAN-based random pairs. We provide new two-sample divergence-based hypothesis tests and evaluate their performance using Monte Carlo experiments. Finally, we apply the new tests to real PolSAR images to evaluate the changes caused by urbanization processes in the Los Angeles and California regions. The results show that our proposals are able to detect changes in PolSAR images. [ABSTRACT FROM AUTHOR]