An incremental-learning neural network for the classification of remote-sensing images

A novel classi®er for the analysis of remote-sensing images is proposed. Such a classi®er is based on Radial Basis Function (RBF) neural networks and relies on an incremental-learning technique. This technique allows the periodical acquisition of new information whenever a new training set becomes available, while preserving the knowledge learnt by the network on previous training sets. In addition, in each retraining phase, the network architecture is automatically updated so that new classes may be considered. These characteristics make the proposed neural classi®er a promising tool for several remote-sensing applications. Ó 1999 Elsevier Science B.V. All rights reserved.