Change detection of high resolution remote sensing image alteration based on multi-feature mixed kernel SVM model

doi:10.6046/gtzyyg.2019.01.03

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Abstract

In view of the fact that different kernel functions have greatly different performance on the same feature, the authors propose a new method of change detection of multi-feature hybrid kernel support vector machine (SVM) model. According to the different characteristics of the change detection, the authors extract image features, make use of the multi-kernel function of several features, give the methods of constructing multi-feature and mixed-kernel function, construct change detection model of multi-feature mixed-nuclear support vector machine, and fully tap the integrity and accuracy of the varying target. The experimental results show that this method makes use of the information of various features. The detection precision is obviously higher than that of the single feature. The method not only takes advantage of extracting change information of small samples, but also avoids the complexity and uncertainty of the old detection method for determining the change threshold.

Keywords object oriented change detection multi-feature mixed kernel support vector machines (SVM)

TP751

Issue Date: 15 March 2019

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	Yizhi LIU
	Huarong LAI
	Dingwang ZHANG
	Feipeng LIU
	Xiaolei JIANG
	Qing’an CAO

Cite this article:

Yizhi LIU,Huarong LAI,Dingwang ZHANG, et al. Change detection of high resolution remote sensing image alteration based on multi-feature mixed kernel SVM model[J]. Remote Sensing for Land & Resources, 2019, 31(1): 16-21.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.03 OR https://www.gtzyyg.com/EN/Y2019/V31/I1/16

Fig.1 Flow chart of multi feature hybrid kernel SVM change detection model

Fig.2 City images

Fig.3 Detection results of SVM with different feature combinations

Tab.1 Accuracy assessment of detection results of SVM with different feature combinations

Fig.4 Comparison of detection results of different kernel functions

Tab.2 Comparison of detection results accuracy of different kernel functions

Tab.3 Comparison of the accuracy of change detection algorithms(%)

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