A Distance-based Kernel Change Detection Algorithm
نویسندگان
چکیده
This paper proposes a distance-based kernel change detection algorithm (DKCD). The input vectors from two images of different times are mapped into a potentially much higher dimensional feature space 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 computational load. Finally this paper takes the soft margin single-class support vector machine to select the optimal hyper-plane with maximum margin. Preliminary results show the distance-based kernel change detection algorithm (DKCD) has excellent performance in speed and accuracy.
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