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

Several applications of supervised classification of remote-sensing images involve the periodical mapping of a fixed set of land-cover classes on a specific geographical area. These applications require the availability of a training set (and hence of ground-truth information) for each new image analyzed. However, the collection of ground truth information is a complex and expensive process that only in few cases can be performed every time that a new image is acquired. This represents a serious drawback of classical supervised classifiers. In order to overcome such a drawback, an unsupervised retraining technique for supervised maximum-likelihood (ML) classifiers is proposed in this paper. Such a technique, which is based on the Expectation-Maximization (EM) algorithm, allows the statistical parameters of an already trained ML classifier to be updated so that a new image, for which a training set is not available, can be classified with an acceptable accuracy. Experiments, which have ...