文献类型：期刊文献

英文题名：The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data

作者：Zhao, Lei[1,2] Chen, Erxue[1,2] Li, Zengyuan[1,2] Fan, Yaxiong[1,2] Xu, Kunpeng[1,2]

第一作者：赵磊

通信作者：Chen, ER[1];Chen, ER[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Chinese Acad Forestry, Key Lab Forestry Remote Sensing & Informat Syst, NFGA, Beijing 100091, Peoples R China

年份：2022

卷号：14

期号：3

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20220511569393);Scopus(收录号：2-s2.0-85123740925);WOS:【SCI-EXPANDED(收录号:WOS:000759961800001)】；

基金：Funding: This study was financially supported by the National Natural Science Foundation of China under grant 41801289, and in part by the Central Public-Interest Scientific Institution Basal Research Fund of China under grant CAFYBB2019SY026, and in part by the National Science and Technology Major Project of China’s High Resolution Earth Observation System under grant 21-Y20B01-9001-19/22.

语种：英文

外文关键词：polarimetric SAR; radiometric terrain correction; supervised classification; angular variation effect

摘要：The radiometric terrain correction (RTC) is an essential processing step for supervised classification applications of polarimetric synthetic aperture radar (PolSAR) over mountainous areas. However, the current angular variation effect (AVE) correction methods of three-step RTC processing are difficult to apply to PolSAR supervised classification because of the problem of interdependence between AVE correction and classification. To address this issue, based on the three-step semi-empirical RTC approach, we propose an improved AVE correction method suitable for the supervised classification of PolSAR. We make full use of the prior knowledge required for supervised classification and RTC processing, that is, samples and elevation data, to calculate the parameters of AVE correction by constructing a weight coefficient matrix. GaoFen-3 QPSI (C-band, quad-polarization) data were used to verify the proposed method. Experimental results showed that the proposed method is available and effective for PolSAR supervised classification. The new method can effectively remove the AVE effect in the PolSAR image, and the overall accuracy of PolSAR supervised classification can be improved about 9% compared to that without AVE correction. For the fine classification of forest types, the AVE correction can improve the classification accuracy by about 20%.