A Multi-component Decomposition Method for Polarimetric SAR Data

There are more unknowns than equations to solve for previous four-component decomposition methods. So they have to determine each scattering power with some assumptions and avoid negative powers in decomposed results with physical power constraints. This paper presents a multi-component decomposition for multi-look Polarimetric SAR (PolSAR) data by combining the Generalized similarity parameter (GSP) and the eigenvalue decomposition. It extends the existing fourcomponent decomposition by adding the diffuse scattering as the fifth scattering component considering additional cross-polarized power that could represent terrain effects and rough surface scattering. And unlike the previous methods, the new method determines the volume scattering contribution by a modified nonnegative eigenvalue decomposition method and utilizes the GSP to determine the negative powers of the three scattering contributions (i.e., odd-bounce, double-bounce, and diffuse scattering) directly without extra assumptions and constraints. By experiment, the new method is proved to be more straightforward and reasonable.