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 four-component 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.