On Characterizing High-Resolution SAR Imagery Using Kernel-Based Mixture Speckle Models

At high resolution, synthetic aperture radar (SAR) speckle tends to be non-Gaussian distributed and diversely textured. Many parametric speckle distributions have been developed to fit specific in-scene content. In contrast, mixture models offer an empirical approximation with the potential to fit arbitrary variations. In this letter, we investigate the feasibility and the efficiency of using finite mixture models of an identical parametric kernel to characterize the wide range of high-resolution speckle. We evaluate and compare the capability of mixture fitting with gamma,
$\mathcal{K}$
, and
$\mathcal{G}^{0}$
kernels against various scene types. Despite the characterization disparity among these base kernels, we show that using any of them in a mixture setting rapidly improves speckle modeling. Finite gamma mixtures, even with a simple kernel form, are applicable to high-resolution SAR imagery for consistent description of complex textured speckle variations.