Five-Component Decomposition Methods of Polarimetric SAR and Polarimetric SAR Interferometry Using Coupling Scattering Mechanisms

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Abstract

In this article, five-component model-based decomposition methods of polarimetric synthetic aperture radar (PolSAR) (P5SD) and polarimetric synthetic aperture radar interferometry (PolInSAR) (PI5SD) using coupling scattering mechanisms are proposed. The target of these methods is to overcome the overestimation of volume scattering and to mitigate the mixed ambiguity of the scattering mechanism. First, coupling scattering models are proposed to decompose the coherency matrix into five submatrices. According to the traditional seven-component decomposition method proposed by Singh et al., the helix and mixed dipole can be merged into a coupling component, while the oriented dipole and compound dipole can also be generated in a general form. Then, the refined volume scattering models of PolSAR are introduced to characterize the volume scattering power of various land covers. The polarimetric interferometric similarity parameter (PISP) is introduced to modify the refined volume scattering models, and it can be used to further reduce the volume scattering contribution of building areas with large orientation angles. In addition, a feature named Θ _{dv}, total power, and PISP are embedded in the building area identification, and the building extraction results are used as the branch condition of the volume scattering model selection. In this article, the validity of P5SD and PI5SD can be demonstrated using airborne L-band E-SAR PolInSAR datasets and airborne C-band PolInSAR data collected by the Aerospace Information Research Institute, Chinese Academy of Sciences. The experimental results demonstrate that the P5SD and PI5SD can be used to effectively interpret the scattering characteristics of the ambiguous regions.

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Wang, Y., Yu, W., & Hou, W. (2021). Five-Component Decomposition Methods of Polarimetric SAR and Polarimetric SAR Interferometry Using Coupling Scattering Mechanisms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 6662–6676. https://doi.org/10.1109/JSTARS.2021.3071161

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