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Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images

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2 Author(s)
L. Bruzzone ; Dept. of Civil & Environ. Eng., Trento Univ., Italy ; D. F. Prieto

An unsupervised retraining technique for a maximum likelihood (ML) classifier is presented. The proposed technique allows the classifier's parameters, obtained by supervised learning on a specific image, to be updated in a totally unsupervised way on the basis of the distribution of a new image to be classified. This enables the classifier to provide a high accuracy for the new image even when the corresponding training set is not available

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:39 ,  Issue: 2 )