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Proceedings Paper

Neural classification guided by background knowledge
Author(s): Jerzy J. Korczak; Denis Blamont; F. Hammadi
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Paper 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.

Paper Details

Date Published: 30 December 1994
PDF: 10 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196716
Show Author Affiliations
Jerzy J. Korczak, Univ. Louis Pasteur (France)
Denis Blamont, Univ. Louis Pasteur (France)
F. Hammadi, Univ. Louis Pasteur (France)

Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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