复数域最小二乘平差及其在PolInSAR植被高反演中的应用

Criterion of Complex Least Squares Adjustment and Its Application in Tree Height Inversion with PolInSAR Data

目前涉及复数观测的数据处理时,主要还是依据观测过程,分步或直接解算,不能考虑观测误差、多余观测信息等。针对这一情况,本文介绍了复数域中数据处理的最小二乘方法,试图将测量平差从实数域推广到复数域,并定量研究了两种平差准则的优劣性。为了了解复数域最小二乘的有效性,以极化干涉SAR植被高反演为例,建立了复数域平差函数模型和随机模型,构建了复数域最小二乘法反演植被高。结果表明该算法反演的植被高结果可靠,其精度优于经典植被高反演算法,且计算简单,易于实现。

At present, data processing methods involving complex observations are mainly step- by-step or direct solver based on the observation process which cannot consider observation errors, redundant observation and so on. For this situation, this paper introduces least squares methods of complex data processing and tries to extend surveying adjustments from the real number space to the complex number space. Meanwhile, the two adjustment criteria in complex domain are compared quantitatively. In order to understand effectiveness of complex least squares, the tree height inversion from PolInSAR data is taken as an example. We firstly establish complex adjustment function model and stochastic model for PolInSAR tree height inversion and apply complex least squares method to estimate tree height. The results show that the complex least squares approach is reliable and better than other classic tree height retrieval methods. Besides, the method is simple and easy to realize.