Neural classification guided by background knowledge

Jerzy J. Korczak; Denis Blamont; F. Hammadi

doi:10.1117/12.196716

30 December 1994 Neural classification guided by background knowledge

Jerzy J. Korczak, Denis Blamont, F. Hammadi

Proceedings Volume 2315, Image and Signal Processing for Remote Sensing; (1994) https://doi.org/10.1117/12.196716
Event: Satellite Remote Sensing, 1994, Rome, Italy

Abstract

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.

Citation Download Citation

Jerzy J. Korczak, Denis Blamont, and F. Hammadi "Neural classification guided by background knowledge", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); https://doi.org/10.1117/12.196716

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