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 Ape...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.展开更多
In this paper, we propose the novel method of complex least squares adjustment （CLSA） to invert vegetation height accurately using single-baseline polarimetric synthetic aperture radar interferometry （PollnSAR） da...In this paper, we propose the novel method of complex least squares adjustment （CLSA） to invert vegetation height accurately using single-baseline polarimetric synthetic aperture radar interferometry （PollnSAR） data. CLSA basically estimates both volume-only coherence and ground phase directly without assuming that the ground-to-volume amplitude radio of a particular polarization channel （e.g., HV） is less than -10 dB, as in the three-stage method. In addition, CLSA can effectively limit errors in interferometric complex coherence, which may translate directly into erroneous ground-phase and volume-only coherence estimations. The proposed CLSA method is validated with BioSAR2008 P-band E-SAR and L-band SIR-C PollnSAR data. Its results are then compared with those of the traditional three-stage method and with external data. It implies that the CLSA method is much more robust than the three-stage method.展开更多
文摘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.
基金supported by the National Basic Research Program of China(Grant No.2013CB733303)National Natural Science Foundation of China(Grant Nos.41274010,41371335)supported by PA-SB ESA EO Project Campaign of"Development of methods for Forest Biophysical Parameters Inversion Using POLIn SAR Data"(Grant No.ID.14655)
文摘In this paper, we propose the novel method of complex least squares adjustment （CLSA） to invert vegetation height accurately using single-baseline polarimetric synthetic aperture radar interferometry （PollnSAR） data. CLSA basically estimates both volume-only coherence and ground phase directly without assuming that the ground-to-volume amplitude radio of a particular polarization channel （e.g., HV） is less than -10 dB, as in the three-stage method. In addition, CLSA can effectively limit errors in interferometric complex coherence, which may translate directly into erroneous ground-phase and volume-only coherence estimations. The proposed CLSA method is validated with BioSAR2008 P-band E-SAR and L-band SIR-C PollnSAR data. Its results are then compared with those of the traditional three-stage method and with external data. It implies that the CLSA method is much more robust than the three-stage method.
