运用EO-1 Hyperion数据和单类支持向量机方法提取岩性信息

411国家重点基础研究发展计划(2009CB219302)资助收稿日期: 2011-06-17; 修回日期: 2011-08-22; 网络出版日期: 2012-02-24网络出版地址: /kcms/detail/11.2442.N.20120224.1047.011.html北京大学学报(自然科学版), 第48卷, 第3期, 2012年5月Acta Scientiarum Naturalium Universitatis Pekinensis, Vol. 48, No. 3 (May 2012)运用EO-1 Hyperion 数据和单类支持向量机方法提取岩性信息张西雅 徐海卿 李培军†北京大学地球与空间科学学院遥感与地理信息系统研究所, 北京 100871; †通信作者, E-mail: pjli@摘要 将扩展的单类支持向量机方法运用到高光谱岩性识别中, 并分析和评价该方法的性能。

利用单类支持向量机分别提取各个感兴趣的岩性类别, 对于被识别为多个岩性类别的像元, 根据该像元与每个单类支持向量机所确定的分类超平面的距离来确定属于哪一类别, 这样, 利用扩展的单类支持向量机来可提取多个感兴趣的岩性类别。

将该方法运用到新疆准噶尔地区的EO-1 Hyperion 高光谱数据岩性分类中, 并与传统的光谱角制图方法进行比较。

结果表明, 扩展的单类支持向量机方法的岩性分类精度显著高于光谱角制图方法, 是一种新的可用于高光谱数据的岩性分类方法。

关键词 高光谱; 单类支持向量机; 光谱角制图; 岩性分类 中图分类号 P627Lithologic Mapping Using EO-1 Hyperion Data and Extended OCSVMZHANG Xiya, XU Haiqing, LI Peijun Key words hyperspectral data; OCSVM; SAM; lithologic classification参考文献†Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871;† Corresponding author, E-mail: pjli@Abstract An extended one-class support vector machine (OCSVM) was applied to lithologic mapping from the EO-1 Hyperion hyperspectral data, and it has been evaluated in terms of classification accuracy. First OCSVM was separately used to extract each lithologic unit of interest. The pixel which was classified to different classes simultaneously was then assigned as the class with smallest distance to the hyperplane. In this way, the extended OCSVM can be used for extracting several lithologic units of interest. The extended OCSVM method was used in lithologic classification from the EO-1 Hyperion hyperspectral data in Junggar area, Xinjiang and compared with the spectral angle mapper (SAM) method. The results showed that the extended OCSVM method outperformed the SAM method in lithologic classification. The extended OCSVM is a useful and effective method for lithologic classification from hyperspectral remote sensing data.[1] Chica-Olmo M, Arbarca-Hernandez F. Computinggeostatistical image texture for remotely sensed data classification. Computers & Geosciences, 2000, 26(4): 373–383[2] Schetselaar E M, Chung J F, Kim K E. Integrationof Landsat TM, Gamma-ray, magnetic, and field data to discriminate lithological units in vegetated granite-gneiss terrain. Remote Sensing of Environment, 2000, 71: 89–105[3] Rowan L C, Mars J C. Lithologic mapping in themountain pass, California area using advanced spaceborne thermal emission and reflection北京大学学报(自然科学版) 第48卷412 radiometer (ASTER) data. 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