On the Use of a New Additive Kernel for Change Detection using SVM
In the context of change detection and due to the multitude of change scenarios, the objective is to build a generic change detection system. For many technical and operational reasons the Support Vector Machines (SVM) algorithm is used. One of the crucial steps when using the SVM algorithm is the choice of the kernel function. With the lack of a priori information the choice of the kernel function may be difficult for the user. In this paper several techniques for constructing a suitable kernel function obtained from the data are proposed.