Neural classification guided by background knowledge
Jerzy J. Korczak, Denis Blamont, F. Hammadi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
The problem discussed in the paper concerns the elaboration, in a very complex landscape, of a cartographical map, using remote sensing data and partial ground-truth knowledge. Maps are created by the neural classification process, regarded as being made up of a sequence of dependent self-organizing phases. To guide the process of classification a background knowledge is to be proposed. The aim is to explore how background knowledge can be integrated into a neural network classifier, and support the classification process. Class descriptions obtained by the method are substantially better than those obtained by the classical backpropagation algorithm. The elaborated maps are at least as good as the maps generated by the classical supervised algorithms.