Model-Based Polarimetric Decomposition With Higher Order Statistics

This letter presents a new general framework for solving polarimetric target decompositions that extends them to use more statistical information and include radar texture models. Polarimetric target decomposition methods generally have more physical parameters than equations and are, thus, underdetermined and have no unique solution. The common approach to solve them is to make certain assumptions, thus fixing some parameters, allowing the other parameters to be solved freely. This letter explains how to obtain additional equations from several statistical moments to find unique solutions and to address the issue of textured product models. The current work extends our previous conference works [1]-[3]. Preliminary results are demonstrated for a well-known real polarimetric synthetic aperture radar scene for the three-component Freeman-Durden decomposition.