Landsat TM multispectral classification using support vector machine method in low-hill areas

Shuhe Zhao; Changqing Ke; Xieqiong Dong; Juliang Li; Xuezhi Feng

doi:10.1117/12.578667

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10 January 2005 Landsat TM multispectral classification using support vector machine method in low-hill areas

Shuhe Zhao, Changqing Ke, Xieqiong Dong, Juliang Li, Xuezhi Feng

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Proceedings Volume 5657, Image Processing and Pattern Recognition in Remote Sensing II; (2005) https://doi.org/10.1117/12.578667
Event: Fourth International Asia-Pacific Environmental Remote Sensing Symposium 2004: Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2004, Honolulu, Hawai'i, United States

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.

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Shuhe Zhao, Changqing Ke, Xieqiong Dong, Juliang Li, and Xuezhi Feng "Landsat TM multispectral classification using support vector machine method in low-hill areas", Proc. SPIE 5657, Image Processing and Pattern Recognition in Remote Sensing II, (10 January 2005); https://doi.org/10.1117/12.578667

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