1.School of Computer Science, China University of Geosciences(Wuhan), Wuhan 430074, China 2.Jiangxi Nuclear Industry Institute of Surveying and Mapping, Nanchang 330038, China 3.Guangdong United to the Real Estate Assessment Survey and Design Co. Ltd., Shaoguan 512100, China 4.Dongguan Zhenjiang Industrial Transfer Industrial Park Management Committee, Shaoguan 512100, China
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.
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2019, Vol. 31 
