Multi-class classification for Wuhan area's TM image based on support vector machine

Authors:
Liu Liu;Zhengjun Huang;Xiaojun Tan;Zhiyuan Zeng
Affiliations:
Digital Engineering and Simulation Research Center, Huazhong University of Science and Technology, Wuhan, China;Digital Engineering and Simulation Research Center, Huazhong University of Science and Technology, Wuhan, China;Digital Engineering and Simulation Research Center, Huazhong University of Science and Technology, Wuhan, China;Digital Engineering and Simulation Research Center, Huazhong University of Science and Technology, Wuhan, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Year:
2009

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Abstract

This paper proposes a multi-class classification method based on Support Vector Machine (SVM), with an emphasis on classes of Wuhan area's water resources. First, this method builds a SVM model by selecting proper testing sample data of Wuhan area's TM image. Then, the image is classified as 5 classes based on the algorithm of SVM model. The experimental results show that this method has obvious advantages in accuracy, compared with the traditional method-Maximum likelihood, especially on classes of water resources.