文献类型：期刊文献

中文题名：Estimation of Crop Biomass Using GF-3 Polarization SAR Data Based on Genetic Algorithm Feature Selection

作者：Kunpeng XU[1] Lei ZHAO[1] Kun LI[2] Erxue CHEN[1] Wangfei ZHANG[3] Hao YANG[4]

第一作者：Kunpeng XU

机构：[1]Institute of Forest Resources Information Technique,Chinese Academy of Forestry,Beijing 100091,China;[2]Beijing Institute of Spacecraft System Engineering,Beijing 100094,China;[3]College of Forestry,Southwest Forestry University,Kunming 650224,China;[4]Beijing Research Center for Information Technology in Agriculture,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China

年份：2020

卷号：3

期号：4

起止页码：126-136

中文期刊名：测绘学报：英文版

基金：National Key R&D Program of China(No.2017YFB0502700);Project of The Technique of Accurate Surface Parameters Inversion Using GF-3 Images(No.03-Y20A11-9001-15/16);National Natural Science Foundation of China(No.41801289)。

语种：英文

中文关键词：Polarization SAR;estimation of crop biomass;genetic algorithm;feature selection;GaoFen-3

分类号：S126

摘要：In recent years,Polarization SAR(PolSAR)has been widely used in the filed of crop biomass estimation.However,high dimensional features extracted from PolSAR data will lead to information redundancy which will result in low accuracy and poor transfer ability of the estimation model.Aiming at this problem,we proposed a estimation method of crop biomass based on automatic feature selection method using genetic algorithm(GA).Firstly,the backscattering coefficient,the polarization parameters and texture features were extracted from PolSAR data.Then,these features were automatically pre-selected by GA to obtain the optimal feature subset.Finally,based on this subset,a support vector regression machine(SVR)model was applied to estimate crop biomass.The proposed method was validated using the GaoFen-3(GF-3)QPSΙ(C-band,quad-polarization)SAR data.Based on wheat and rape biomass samples acquired from a synchronous field measurement campaign,the proposed method achieve relative high validation accuracy(over 80%)in both crop types.For further analyzing the improvement of proposed method,validation accuracies of biomass estimation models based on several different feature selection methods were compared.Compared with feature selection based on linear correlation,GA method has increased by 5.77%in wheat biomass estimation and 11.84%in rape biomass estimation.Compared with the method of recursive feature elimination(RFE)selection,the proposed method has improved crops biomass estimation accuracy by 3.90%and 5.21%,respectively.