A new method for extracting mineralization information from remote sensing image based on Support Vector Machines (SVM) is presented in this paper. According to the field measured spectral data of mineralized alteration rocks and wall rocks, the authors first extracted the training examples by Spectral Angle Mapper (SAM), and then selected the RBF as the kernel function. After that, cross-validation algorithm was applied to seek superior SVM model parameters. This model was used to extract mineralization information from remote sensing image in Mangya area, Qinghai province. Practice has proved that this method is effective in extracting mineralization information.
傅文杰, 洪金益, 朱谷昌. 基于SVM遥感矿化蚀变信息提取研究[J]. 国土资源遥感, 2006, 18(2): 16-19.
FU Wen-Jie, HONG Jin-Yi, ZHU Gu-Chang. THE EXTRACTION OF MINERALIZED AND ALTERED ROCK
INFORMATION FROM REMOTE SENSING IMAGE BASED ON SVM. REMOTE SENSING FOR LAND & RESOURCES, 2006, 18(2): 16-19.