Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination

doi:10.6046/gtzyyg.2011.02.04

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Abstract In consideration of the features of remote sensing image, this paper presents a new method for classification of remote sensing images based on multiple classifiers combination. In this method, three supervised classifications, Mahalanobis Distance, Maximum Likelihood and SVM, which are of more precision and better diversity in classification, are selected to serve as the sub-classifications, and the simple vote classification, maximum probability category method and fussy integral method are combined together according to certain rules. The authors adopted Huairen county in Shanxi as the study area for land use classification using color infrared aerial images. Experimental result showed that the overall classification accuracy was improved by 12% and Kappa coefficient was increased by 0.12 in comparison with SVM classification which has the highest accuracy in single sub-classifications. This result indicates that the classification of multiple classifiers combination is an effective classification method.

Keywords Landsat TM Land surface temperature Land surface emissivity Mono-window algorithm Single channel algorithm

TP 75

Issue Date: 17 June 2011

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	QIN Zhi-hao
	LI Wen-juan
	XU Bin
	CHEN Zhong-xin
	LIU Jia

Cite this article:

QIN Zhi-hao,LI Wen-juan,XU Bin, et al. Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 19-25.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.02.04 OR https://www.gtzyyg.com/EN/Y2011/V23/I2/19

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