Abstract
Abstract. Based on the support vector machine (SVM) tools and multiple kernel method, the combinations of kernel functions were mainly discussed. The construction method of image differencing kernel with multi-feature (spectral feature and textural feature) has been developed. Through this method and weighting of the categories' samples, the improved SVM change detection model has been proposed, which could realize the direct extraction of spatial distribution information from several change classes. From the experiments we can draw the following conclusions: with the help of multiple kernel function integrating spectral features and texture information, the new change detection model can achieve higher detection accuracy than the traditional methods and is suitable for the small-sample experiment. Furthermore, it avoids the complex and uncertainty in determining change threshold required in the old detection methods.
Highlights
As for remote sensing image change detection, it is feasible in many application areas to construct the decision hyperplane based on single kernel function in feature space
With the multiple kernel method, the integration of remote sensing image multi-feature such as spectral and textural feature, an improved support vector machine (SVM) change detection model based on multi-feature differencing kernel was proposed, in which a multi-kernel function on multi-feature space was combined after applying an independent kernel function for different feature space respectively
Based on the introduction and definitions about the multi-feature SVM above, with the multiple kernel method and the integration of remote sensing image multi-feature, we could propose an improved support vector machine (SVM) change detection model, in which a multi-kernel function on multi-feature space was combined after applying an independent kernel function for different feature space respectively
Summary
As for remote sensing image change detection, it is feasible in many application areas to construct the decision hyperplane based on single kernel function in feature space. Different feature space cannot be effectively described by single kernel function [1, 2]. It will cause a large amount of computation if classification is handled with many combined types of feature space, or if the sample feature contains heterogeneous information. With the multiple kernel method, the integration of remote sensing image multi-feature such as spectral and textural feature, an improved support vector machine (SVM) change detection model based on multi-feature differencing kernel was proposed, in which a multi-kernel function on multi-feature space was combined after applying an independent kernel function for different feature space respectively.
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