Abstract-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. Index Terms-Expectation maximization algorithm, land-cover map updating, maximum likelihood (ML) classification, remote sensing, unsupervised retraining.
In the context of remote sensing, an unsupervised retraining technique for a Gaussian maximum likelihood classifier is presented in REF ).
Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images
IEEE Trans. Geoscience and Remote Sensing
IEEE Trans. Geoscience and Remote Sensing
Computer Science Mathematics