Abstract — This paper presents an advanced approach for landcover change detection in remote-sensing imagery. Firstly, several supervised neural network change detection techniques have been considered and evaluated versus statistical supervised ones.; the chosen neural network models are Multilayer Perceptron (MLP), Radial Basis Function Neural Network (RBF), and Supervised Self Organizing Map (SOM), whereas the applied statistical classifiers are Bayes and Nearest Neighbor (NN). Secondly, we have investigated the following unsupervised change detection techniques: Self-Organizing Map (SOM) (neural clustering), versus K-means (statistical clustering), and Fuzzy C-means (FCM) (fuzzy clustering). The proposed model of change detection in multispectral satellite images has two main processing stages: (a) feature selection (using one of the three techniques: the concatenation of corresponding pixels (CON), the computation of absolute differences betwee
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