Classification of imbalanced remotesensing data by neural networks

The multilayer perceptron neural network has proved to be a very effective tool for the classification of remote-sensing images. Unfortunately, the training of such a classifier by using data with very different a priori class probabilities .imbalanced data is very slow. This paper describes a learning technique aimed at speeding up the training of a multilayer perceptron when applied to imbalanced data. The results obtained on an optical remote-sensing data set suggest that not only is the proposed technique effective in terms of training speed but it also allows classification results to be more stable wit