We propose a supervised nonparametric technique, based on the "compound classification rule" for minimum error, to detect land-cover transitions between two remote-sensing images acquired at different times. Thanks to a simplifying hypothesis, the compound classification rule is transformed into a form easier to compute. In the obtained rule, an important role is played by the probabilities of transitions, which take into account the temporal dependence between two images. In order to avoid requiring that training sets be representative of all possible types of transitions, we propose an iterative algorithm which allows the probabilities of transitions to be estimated directly from the images under investigation. Experimental results on two Thematic Mapper images confirm that the proposed algorithm may provide remarkably better detection accuracy than the "Post-Classification Comparison" algorithm, which is based on the separate classifications of the two images.
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.
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