A minimum-cost thresholding technique for unsupervised change detection

We propose an automatic thresholding technique for difference images in uns upervised change detection. Such a technique takes into account the differe nt costs that may be associated with commission and omission errors in the selection of the decision threshold. This allows the generation of maps in which the overall change-detection cost is minimized, i.e. the more critica l kind of error is reduced according to end-user requirements.