基于S-RVoG模型的PolInSAR森林高度非线性复数最小二乘反演算法

针对经典的PolInSAR森林高度三阶段几何反演算法在单基线条件容易受到地体幅度比假设以及地形坡度影响的问题，从测量平差角度提出了基于S-RVoG模型的PolInSAR非线性复数最小二乘森林高度反演算法。该算法不再需要假设某一个极化通道地体幅度比为零，且采用考虑地形坡度影响的S-RVoG模型作为平差模型。为了验证算法，本文采用欧空局BioSAR2008项目提供的3景P波段极化干涉SAR数据进行两组单基线森林高度反演试验。结果表明，在单基线条件下，基于RVoG模型的非线性复数最小二乘算法反演结果优于三阶段几何反演算法，而基于S-RVoG模型的非线性复数最小二乘算法进一步提高反演精度，对于坡度较大区域（坡度>10°），精度平均提高了18.48%。

A S-RVoG model-based PolInSAR nonlinear complex least squares method for forest height inversion

The classical three-stage forest height geomatical inversion method is easily affected by the assumption of the amplitude ratio of ground-to-volume scattering (GVR) and terrain slope. To address these problems, from the perspective of survey adjustment, the S-RVoG (slope-random volume over ground) based nonlinear complex least squares forest height inversion method is proposed in this paper. On the one hand, it does not need to hold the GVR assumption. On the other hand, it can take into account the terrain slope effect by adopting the S-RVoG model as the adjustment model. Three scenes of P-band PolInSAR data acquired from ESA BioSAR2008 campaign are used to construct two groups of single baseline tests for forest height inversion. The results show the RVoG-based nonlinear complex least squares method can obtain better forest height results than the three-stage geometrical method in a single baseline configuration. The proposed S-RVoG based nonlinear complex least squares method can further improve the accuracy. The improvement reaches a stand-level mean of 18.48% for slopes greater than 10°.