A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images

摘要

A partially unsupervised approach to the classification of multitemporal remote-sensing images is presented. Such an approach allows the automatic classification of a remote-sensing image for which training data are not available, drawing on the information derived from an image acquired in the same area at a previous time. In particular. The proposed technique is base on a cascade-classifier approach and on a specific formulation of the expectation-maxi- mization(EM) algorithm used for the unsupervised estimation of the statistical parameters of the image to be classified the results of experiments carried out on a multitemporal data set confirm the validity of the proposed ap- proach.