Evolving Topologies of Artificial Neural Networks Adapted to Image Processing Tasks

Artiicial Neural Networks (ANN) have gained increasing popularity as an alternative to statistical methods for classiication of Remote Sensing Data. Their superiority to some of the classical statistical methods has been shown in the literature. Therefore, ANNs are commonly used for segmentation and classiication purposes. We address the problem of generating an appropriate network topology, the right number of training epochs and preprocessing the training data set for multi-layer feed-forward ANNs. A method based on Genetic Algorithms (GA) for the automatic generation of problem{adapted topologies is employed with the parallel netGEN system which has been designed by the authors. A land cover classiication problem using multi{spectral Landsat Thematic Mapper (TM) data is presented so as to demonstrate the capabilities of netGEN.