Improved Four-Component Model-Based Target Decomposition for Polarimetric SAR Data

Abstract

An improved four-component model-based target decomposition scheme for polarimetric synthetic aperture radar data is proposed in this letter. The reason for the emergence of the negative powers in the Yamaguchi decomposition has been analyzed, and three corresponding additional steps are added in the proposed scheme. First, the orientation angle compensation is applied to the coherency matrix. Second, the coherency matrix with the maximum entropy, i.e., the identity matrix is used as the volume scattering model instead of the traditional ones. Third, corresponding power constraints are appended to the scheme. Moreover, the densely vegetated areas and the residual areas are processed separately via the H/α/A classification in the proposed scheme. Finally, the polarimetric-scattering-characteristic-preserving classification is utilized to verify the improvements of the proposed scheme. To demonstrate the effectiveness of the decomposition, an Advanced Land Observing Satellite Phased-Array-type L-band Synthetic Aperture Radar polarimetric image acquired over Beijing, China, is analyzed, and the results are presented in this letter. With negative powers eliminated by the proposed scheme, improvements can be observed in the experimental results, particularly for the urban areas.