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机载双天线极化干涉SAR反演植被参数得到广泛应用,并取得较好的反演精度.然而对于星载的单天线极化干涉SAR系统,只能采取重轨方式获得极化干涉数据,且该数据受到较为严重的时间去相干的影响.因此,利用星载极化干涉SAR数据反演植被参数不得不考虑时间去相干的影响.由于时间去相干的影响,经典RVoG模型已经被证明对植被区域的极化干涉SAR数据进行反演的结果有较严重的偏差.针对这一问题,基于RVoG模型的TD-RVoG模型被提出,用于消除时间去相干的影响,但该模型未知参数太多,难以应用于单基线极化干涉数据反演植被高度.在原始TD-RVoG模型的基础,提出一种新的运动时间去相干模型,减少了未知参量个数,并采用三阶段法反演植被高度.利用星载重轨极化ALOS/PALSAR数据进行模型验证,结果显示新模型整体反演结果误差在1.5 m以内.
However, for the spaceborne single-antenna polarization interferometry SAR system, only the heavy-rail method can be used to obtain the polarization interference data, Therefore, the inversion of vegetation parameters using space-borne polarimetric SAR data has to consider the influence of time on coherence. Due to the time-dependent coherence, the classical RVoG model has been proved to be effective The results of the inversion of the data from the Polarimetric SAR data in the vegetation area have more serious deviations.For this problem, the TD-RVoG model based on the RVoG model is proposed to eliminate the influence of time de-coherence, but the unknown parameter of this model is too It is difficult to apply the single baseline polarization inversion data to retrieve the vegetation height.Based on the original TD-RVoG model, a new motion-time de-coherence model is proposed to reduce the number of unknown parameters and use the three-phase inversion method Vegetation height. The model validation using satellite-borne orbital polarization ALOS / PALSAR data shows that the overall model error of the new model is within 1.5 m.