A Neural System for Remote Sensing Multispectral Image Classification

Abstract

This work presents a system for Remote Sensing (RS) multispectral image classification based on Artificial Neural Networks (ANN), aiming at two objectives, namely: searching of techniques for improving the performance in the classification task and to exploit the advantages of unsupervised learning for feature extraction. The system is divided in two phases: feature extraction by the Kohonen Self -Organizing Map (SOM) and classification by a Multilayer Perceptron (MLP) network, trained by a learning algorithm which uses 2nd-order information exactly calculated. To evaluate the efficiency of this classification scheme, a comparative analysis with the maximum likelihood algorithm, conventionally used for RS multispectral images classification, is realized.