CORSISCA: classification of remotely sensed images-a soft computing approach

The classification of multispectral satellite images is a challenging problem and has a number of applications such as feature identification, change detection, etc. We apply modified neural network algorithms: GA-BP (genetic algorithm as precursor to the back propagation) and modular artificial neural network (MNN) to classify the LISS-3 image of Allahabad area. We also classify the resolution merged image (USS-3 with PAN) using the same algorithms. By using genetic algorithm as a precursor to ANN, we increase the probability of reaching to the global minimum, thus reducing the problem of a stuck neural network in the local minimum. MNN models the human brain more closely to apply task decomposition to the satellite images as well. The output of the above techniques are generated and analyzed.