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Proceedings Paper

Evolutionary support vector machine and its application in remote sensing imagery classification
Author(s): Yan Guo; Lishan Kang; Fujiang Liu; Linlu Mei
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Paper Abstract

The regularization parameter and the kernel parameters greatly affect the performance of support vector machines (SVM) models. This paper proposes an evolutionary algorithm (EA) to automatically determine the optimal parameters of SVM with the better classification accuracy and generalization ability simultaneously. The proposed ESVM model, called evolutionary SVM or ESVM, was applied to a Land-cover classification experiment in a 840×840 pixels Landsat-7 Enhanced Thematic Mapper plus (ETM+) high-resolution image of Wuhan in Hubei province of China compared with the conventional SVM model. Experimental results show that the use of EA for finding the optimal parameters results mainly in improvements in overall accuracy and generalization ability in comparison with conventional SVM. It is observed that classification accuracy of up to 91% is achievable for Landsat data produced by ESVM.

Paper Details

Date Published: 26 July 2007
PDF: 8 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67523D (26 July 2007); doi: 10.1117/12.760804
Show Author Affiliations
Yan Guo, China Univ. of Geosciences (China)
Lishan Kang, China Univ. of Geosciences (China)
Fujiang Liu, China Univ. of Geosciences (China)
Linlu Mei, China Univ. of Geosciences (China)

Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information
Weimin Ju; Shuhe Zhao, Editor(s)

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