A comparative study of conventional and neural network classification of multispectral data

The classification of remotely sensed data using several classifiers and neural networks is considered. The study was conducted using a test scene containing mainly agricultural areas. The main result obtained is that the application of topological map based neural networks to classify the intensity vectors issued from agricultural classes are more suited than other neural network methods, especially the multilayer perceptron (MLP) usually employed. Obtained results are very close to those of the maximum likelihood classifier (MLC)