UNSUPERVISED CHANGE DETECTION FOR MULTISPECTRAL IMAGES
This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images. The proposed approach aims at extracting the change information by jointly analyzing the spectral channels of multitemporal images without any training data. This is accomplished by using a selective Bayesian thresholding for deriving a pseudo training set that is necessary for initializing an adequately defined binary semisupervised support vector machine (S3VM) classifier. Starting from these initial seeds, the S3VM performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a semisupervised learning algorithm. The values of the classifier parameters are then defined according to a novel unsupervised model-selection technique based on a similarity measure between change-detection maps obtained with different settings.
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