Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery
We present the work on efficient classification of multispectral images using soft computing approach. Selection of most discriminative spectral bands and determination of the number of hidden layer neurons are the two most critical issues. We proposed a new multiobjective particle swarm optimization based methodology for adaption of neural network structure for pixel classification of Satellite Imagery. It simultaneously estimates the most discriminative spectral features and the optimal number of nodes in hidden layer. Xie-Beni and ? indexes of proposed algorithm are better than MLC and Euclidean Classifier.
neural network particle swarm optimization land cover classification multiobjective optimization (moo) remote sensing imagery