A neural-statistical approach to multitemporal and multisource remote-sensing image classification

A data fusion approach to the classification of multisource and multitemporal remote-sensing images is proposed. The method is based on the application of the Bayes rule for minimum error to the "compound" classification of pairs of multisource images acquired at two different dates. In particular, the fusion of multisource data is obtained by using multilayer perceptron neural networks for a nonparametric estimation of posterior class probabilities. The temporal correlation between images is taken into account by the prior joint probabilities of classes at the two dates. As a novel contribution of this paper, such joint probabilities are automatically estimated by applying a specific formulation of the expectation-maximization (EM) algorithm to the data to be classified. Experiments carried out on a multisource and multitemporal data set confirmed the effectiveness of the proposed approach.