PolSAR Coherency Matrix Decomposition Based on Constrained Sparse Representation

This paper presents a new model-based decomposition method for the polarimetric synthetic aperture radar coherency matrices. We improve the model flexibility from the following two aspects: To reach a compromise between model flexibility and computation complexity, for the volume scattering component, the elementary scatterer shape is allowed to change from sphere/flat plate to dipole, then to dihedral, whereas orientation randomness is simplified by only considering two cases. Different orientation angles are considered for each component. Since the models become more complex, new decomposition procedures are developed. The three-component decomposition is first reformulated as a constrained sparse representation problem. Then, inspired by the orthogonal matching pursuit variant developed by Bruckstein et al. in 2008, new decomposition procedures are designed. The effectiveness of the proposed method is verified using a synthetic data set and two real SAR data sets, including a RADARSAT-2 data set and the NASA/JPL AIRSAR data set over San Francisco Bay.