关键词: 光谱角匹配技术, 支持向量机, 高光谱, 蚀变信息提取, 相似矿物

Abstract: With the development of hyper-spectral remote sensing technology, the level of quantitative remote sensing technology has improved. Aiming at the hyper-spectral image cube, the understanding and data processing in image spatial dimension must be changed to that completed in the spectral dimension. Therefore, an image classification model combined with SAM（Spectral Angle Mapper） and SVM（Support Vector Machine） is introduced, and extracts alteration information in the spectral dimension. In view of the SAM algorithm considering only the spectrum direction, ignoring radiance size, the second classification is made for the SAM results using SVM algorithm and the best parameter is sought using grid search method combined with the classification accuracy assessment. The results of AVIRIS hyper-spectral data show that the classification precision of alteration information reaches 78.172 6%, and a Kappa coefficient of 0.712 5. This model is convenient calculation, and has some practical meaning in solving spectral dimension terrain classification and similar mineral alteration information extraction.

Key words: Spectral Angle Mapper（SAM）, Support Vector Machine（SVM）, hyper-spectral, alteration information extraction, similar mineral