A Kernel Change Detection Algorithm in Remote Sensing Image
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摘要: 提出了一种新的遥感影像核变化检测方法。该方法是将原始空间不同时相的输入矢量通过核函数非线性映射到高维特征空间,然后在高维特征空间中通过传统变化检测方法处理得到新的输入矢量,最后通过半监督的单类支持向量机算法对新的输入矢量构造变化区域与非变化区域的最优分割超平面。试验证实,本文的核变化检测方法具有较高的检测精度和效率。Abstract: A kernel change detection algorithm(KCD) is proposed.The input vectors from two images of different times are mapped into feature space of high dimension via a nonlinear mapping.Which will usually increase the linear separation of change and no-change regions.Then,a simple linear distance measure between two feature vectors of high dimension is defined in features space,which corresponds to the complicated nonlinear distance measure in input space.Furthermore,the distance measure's dot is expressed in the combination of kernel functions and large numbers of dot operations processed in input space not in feature place by combined kernel tactic,which avoids the operation burden in high dimension space.The soft margin single-class support vector machine(SVM) is taken to select the optimal hyper-plane.Results show that KCD has excellent performance in speed and accuracy.
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