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In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Aperture Radar(SAR)decomposition.It uses a Polarimetric Interferometric Similarity Parameter(PISP)calculated from Polarimetric SAR Interferometry(PolInSAR)datasets to the scattering decomposition.The PISP is proposed to reveal the geometric sensitivity of SAR interferometry.It is defined by three optimized mechanisms obtained from PolInSAR datasets,therefore,it not only relates to the coherent scattering mechanism closely,but also sufficiently uses the phase and amplitude information.The PISP of building is high,and forest’s PISP is low.The proposed method uses the PISP as a judge condition to select different vegetation model adaptively.The decomposition results show the proposed method can effectively solve the vegetation ingredients overestimation problem.In addition,it is sensitive to the directional scattering.
In this paper, a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Aperture Radar (SAR) decomposition. It uses a Polarimetric Interferometric Similarity Parameter (PISP) calculated from Polarimetric SAR Interferometry (PolInSAR) datasets to the scattering decomposition. The PISP is proposed to reveal the geometric sensitivity of SAR interferometry. It is defined by three optimized mechanisms obtained from PolInSAR datasets, therefore, it not only relates to the coherent scattering mechanism closely, but also sufficiently uses the phase and amplitude information. PISP of building is high, and forest’s PISP is low. proposed method uses the PISP as a judge condition to select different vegetation model adaptively. the vegetation ingredients overestimation problem.In addition, it is sensitive to the directional scattering.