A modular neural network model for change detection in earth observation imagery

One applies the neural classifier of Concurrent Self-Organizing Maps (CSOM) for change detection in multispectral multi-temporal remote sensing imagery. The present model of change detection has two main processing stages: (a) feature selection using concatenation algorithm (CON); (b) CSOM classifier. CSOM is a supervised neural classifier whose architecture is composed by a collection of small SOM modules, which use a global winner-takes-all strategy. We have compared the performances of CSOM classifier with those of the following benchmark techniques: Nearest Neighbor (NN), Bayes (likelihood classifier), Multilayer Perceptron (MLP), Radial Basis Function neural network (RBF), and Support Vector Machine (SVM). The considered techniques are evaluated using a LANDSAT 7 ETM+ multi-temporal image. One deduces that CSOM leads to best performance of the considered change detection classifiers for independent optimization of any of the two parameters: TSR (Total Success Rate) or MR (Miss Rate).