基于散射体模型的PolInSAR数据模拟反演研究

PolInSAR Data Simulation for Inversion Study Based on Scattering Model

极化干涉合成孔径雷达(PolInSAR)测量是一种集极化雷达(PolSAR)和干涉雷达(InSAR)测量技术于一体的新的对地观测技术,利用极化干涉雷达数据提取地表植被垂直结构参数是当前极化干涉研究的热点问题。然而现在国内外适用于PolInSAR处理的数据都是重复轨道数据,其中应用广泛的是SIR-C/X-SAR数据,其成像时间在10多年前,成像区域植被高度及衰减系数大小无法考证,因此利用该数据进行反演,反演结果的精度无法验证,故有必要对极化干涉数据进行模拟来验证PolInSAR反演算法的优劣。提出了一种便于验证反演算法的基于散射体模型的PolInSAR数据模拟方法,并利用基于统计特征的反演算法验证了该数据的有效性。

Polarimetric interferometric SAR （PolInSAR） is a new advanced technique recently based on measurement techniques of polarimetric SAR and interferometric SAR, and making use of PolInSAR data for retrieving the vertical structure parameters of the vegetation layer becomes the hot research topic of the PolInSAR at present. But the data which has been widely used for PolInSAR processing is acquired in the double-pass model, especially the data from SIR-C/X-SAR system which was imaged ten years ago, and the vegetation parameters can not be exactly obtained, and the inversion precision of this data can not be validated, so it is very necessary to simulate the PolInSAR data to judge of the different inversion algorithms. Anew method was proposed to simulate the PolInSAR data, and the ML inversion algorithm was made to prove the validity of this method.