Unsupervised change detection via hierarchical support vector clustering
When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of nested hierarchical support vector clustering descriptions, select the appropriate one using a cluster validity measure and finally merge the clusters into two classes, corresponding to changed and unchanged areas. Experiments on two multispectral datasets confirm the power and appropriateness of the proposed system.