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Proceedings Paper

Landsat TM multispectral classification using support vector machine method in low-hill areas
Author(s): Shuhe Zhao; Changqing Ke; Xieqiong Dong; Juliang Li; Xuezhi Feng
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Paper Abstract

Support vector machine (SVM) is a newly learning machine. In the paper, it applied the SVM method to research on remote sensing multi-spectral classification using Landsat TM data. It selected the typical low-hill area as study site, which was located on the southern of the Yangze River, China. The land cover types were divided into six categories, which were the waterbody, the construction land, the paddy field, the woodland, the teagarden, and the bare land, etc. The classification of the study site using the Kohonen networks method was also given. The classification results show that classification accuracy of the SVM method is better than that of the Kohonen Networks method. Especially it could discriminate the woodlands from the mountainous shadow. In conclusion, the SVM method could gain higher classification accuracy using smaller training sample in low-hill area. It could also solve the confusion problems among the ground objects.

Paper Details

Date Published: 10 January 2005
PDF: 8 pages
Proc. SPIE 5657, Image Processing and Pattern Recognition in Remote Sensing II, (10 January 2005); doi: 10.1117/12.578667
Show Author Affiliations
Shuhe Zhao, Peking Univ. (China)
Changqing Ke, Nanjing Univ. (China)
Xieqiong Dong, Nanjing Univ. (China)
Ctr. for Agrometeorology and Remote Sensing Application of Yunnan (China)
Juliang Li, Nanjing Univ. (China)
Xuezhi Feng, Nanjing Univ. (China)

Published in SPIE Proceedings Vol. 5657:
Image Processing and Pattern Recognition in Remote Sensing II
Yoshifumi Yasuoka; Stephen G. Ungar, Editor(s)

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