利用全极化SAR数据估算裸露土壤表面参数的分解方法

W. Yuan, Q. Qin, S. Du, Xinyi Shen, Hong-bo Jiang, Yan Ma, Shixiong Liu
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引用次数: 2

摘要

本研究旨在证明两种不同的极化目标分解方法可以提高估算裸露土壤表面参数的SAR数据精度。为了实现这一目标,进行了两个实验:(1)对JPL/AIRSAR l波段全偏振数据进行了Freeman和cloud分解方法;(2)采用先进积分方程模型(AIEM)模拟后向散射系数。原始数据σ0hh、σ0vv与模拟数据的均方根误差(rmse)分别为1.96和1.25 dB。但是,如果使用cloud方法对原始数据进行分解,rmse将分别降低到1.45和1.14dB;Freeman方法的均方根误差分别为1.64和1.35 dB。因此,利用极化目标分解补偿,尤其是cloud方法,可以提高SAR数据估算裸土表面参数的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decomposition methods for the estimation of bare soil surface parameters using fully polarimetric SAR data 1
This study wants to demonstrate that two different polarimetric target decomposition methods can improve SAR data accuracy for estimating the parameters of bare soil surface. To achieve this goal, two experiments are conducted: (1) both Freeman and Cloude decomposition methods are performed on JPL/AIRSAR L-band fully polarimetric data; and (2) Advanced Integral Equation Model (AIEM) is used to simulate backscatting coefficients. The root mean square errors (RMSEs) of σ0hh, σ0vv between original data and AIEM simulated data are 1.96 and 1.25 dB. However, if Cloude method is used to decompose original data, the RMSEs will be reduced to 1.45 and 1.14dB, respectively; for Freeman method, the RMSEs are 1.64 and 1.35 dB. Therefore, polarimetric target decomposition compensation, especially Cloude method, can help to improve the accuracy of SAR data for estimating the parameters of bare soil surface.
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