CLASSIFICATION OF MULTISPECTRAL REMOTE-SENSING DATA USING A BACK-PROPAGATION NEURAL NETWORK

The suitability of a back-propagation neural network for classification of multispectral image data is explored. Methodology is developed for selection of both training parameters and data sets for the training phase. A new technique is also developed to accelerate the learning phase. To benchmark the network, the results are compared to those obtained using three other algorithms: a statistical contextual technique, a supervised piecewise linear classifier, and an unsupervised multispectral clustering algorithm. All three techniques were applied to simulated and real satellite imagery. The simulated data allowed study of the functionality of each method over a diverse but controlled environment for a more accurate assessment of performance. The real-world data was included to study the implementation details and the real-world feasibility of the new method. Results from the classification of both Monte Carlo simulation and real imagery are summarized.