Genetic algorithm-based support vector classification method for multi-spectral remote sensing image

Authors:
Yi-Nan Guo;Da-Wei Xiao;Mei Yang
Affiliations:
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Year:
2010

Citing 1
Cited 1

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The nature of statistical learning theory

Multi-spectral remote sensing images classification method based on SVC with optimal hyper-parameters

AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III

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Abstract

The traditional classification methods based on asymptotic theory for multi-spectral remote sensing image need the infinite training samples, which is impossible to be satisfied. Support vector classification (SVC) method based on small samples overcome above difficulty. However, the values of hyperparameters in SVC directly determine the method's performance, which are randomly selected. In order to obtain the optimal parameters, genetic algorithms (GAs) are introduced. Experimental results indicate that this method can not only save time for classification, but also improve the generalization of the SVC model.