文献类型：会议论文

英文题名：Optimal Support Vector Machines for Forest Above-ground Biomass Estimation from Multisource Remote Sensing Data

作者：Guo, Ying[1] Li, Zengyuan[1] Zhang, Xu[1] Chen, Er-xue[1] Bai, Lina[1] Tian, Xin[1] He, Qisheng[] Feng, Qi[1] Li, Wenmen[1]

第一作者：郭颖

通信作者：Guo, Y[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

会议论文集：IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

会议日期：JUL 22-27, 2012

会议地点：Munich, GERMANY

语种：英文

外文关键词：SVM; KNN; RBFNN; BPNN; random forest; forest above-ground biomass; multisource remote sensing estimation

年份：2012

摘要：The main objective of this study was to investigate the potential of using Support Vector Machines (SVM) and Random forest (RF) to estimate forest above ground biomass (FAGB) by using multi-source remote sensing data. To do so, we introduced a basic flow of SVM to estimate FAGB from multisource remote sensing data. RF method was adept at identifying relevant features having main effects in multisource remote sensing data. Results show that: (i) In the stage of feature selection, the Random Forest model provide better results compared to the typical F-scores method. (ii) The optimal SVM model, based on the selection of features clearly demonstrate that the estimation accuracy increased by feature selection algorithm. (iii) Compared to the optimal KNN, BPNN and RBFNN model, the optimal SVM algorithm provided more accurate and robust result on the considered case.