Fusion of multitemporal contextual information by neural networks for multisensor image classification

The analysis of a multitemporal sequence of images of a given site makes it possible to exploit temporal information in addition to spectral and spatial information, and therefore represents a way to improve the accuracy with respect to the non-contextual single-time classification. The proposed contextual multitemporal classification scheme consists of two stages of multilayer perceptron (MLP) neural networks for each single-time image of the multitemporal sequence. The first stage is a one-hidden layer MLP whose roˆle is to estimate, for each pixel, the single-time posterior probability of each class, given the feature vector. These probability estimates represent spectral information; in addition, they are utilized to generate a non-contextual classification map. The neighboring class labels of a given pixel in the non-contextual classification map are exploited to extract spatial information, while temporal information is deduced from the non-contextual maps produced by the remaining single-time images in the multitemporal sequence. Spatial and temporal contextual information together with spectral information serve as inputs for the second stage network of the classification scheme where the fusion takes place. As the network configuration can influence the classification performances, three MLP-based configurations are investigated. Experimental results on a multitemporal data set consisting of two multisensor (Landsat TM and ERS-1 SAR) images are presented. The performances of the proposed methods are discussed and compared with those obtained by a reference classifier based on the Markov random fields fusion approach in terms of classification accuracy. The results show that the proposed fusion approach based on neural networks may represent an interesting solution to the problem of multitemporal contextual fusion