Scattering Power Decomposition Based on PolInSAR Images and Sparse Representation

Scattering Power Decomposition of polarimetric synthetic aperture radar (PolSAR) coherency matrix is essential for PolSAR image interpretation. However, current research faces two challenges: (1) the contradiction between limited scattering models (i.e. covariance matrix models or coherency matrix models) and the complex, diverse scattering types in reality, leading to incomplete and ambiguous characterization; (2) too many manually configured branches in the solving process due to models having more unknown parameters than observations. To address the above issues, a scattering power decomposition method based on Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) image and sparse representation theory is proposed. Specifically, to eliminate scattering ambiguity in polarization data, PolInSAR coherence and self-organizing maps (SOM) are introduced to develop unambiguous decomposition schemes. Besides, to enhance the diversity of scattering mechanisms and models, the rotated double-bounce and coherent volume scattering mechanisms for different types of buildings are added; the scattering power decomposition is implemented through sparse representation with an overcomplete dictionary of diverse scattering models. Experiments on three pairs of PolInSAR data validate the effectiveness of the proposed method. This work reveals the essence of the scattering characterization system, offers effective approaches to address its complexities without extensive scattering modeling, and has valuable applications in target detection and fine land cover classification.