Lithology Division for Large-Scale Region Segmentation Based on LS-SVM and High Resolution Remote Sensing Images
详细信息
- 责任者：Yang Jiajia (College of Geoexploration Science and Technology ; Jilin University ; Changchun 130026 ; China) ; Jiang Qigang
- 刊名：Journal of China University of Petroleum
- 出版年：2012
- 卷期：36(1)
- 关键词：遥感 ; remote sensing ; 最小二乘法 ; least-squares methods ; 支持向量机 ; support vector machine
- 页码：p.60-67
- 图表：5 illus., 4 tables, 18 refs.
- 分类号：1600
- issnNo：ISSN1673-5005
- isbnNo：CN37-1441/TE
摘要
Based on the concept of large-scale region segmentation,extraction of texture,shape,spectral information of high resolution remote sensing image associated with the lithology and the advantages of least squares-support vector machines (LS-SVM) in the non-linear prediction were used in the geological lithology identification. The results show that the classification method based on LS-SVM performs well in the high-resolution remote sensing images lithological identification,and provides a new method and means for the classification of geological lithology.
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