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Proceedings Paper

Rule extraction based on neural networks for satellite image interpretation
Author(s): Laurent Mascarilla
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Paper Abstract

In the frame of an image interpretation system for automatic cartography based on remote sensing image classification improved by a photo interpreter knowledge, we propose a system using neural networks to produce fuzzy production rules. These rules are intended to describe class vegetation context relatively to out image data (generally a G.I.S.) as a human expert could do. In the system, the expert only gives samples of concerned classes via a G.U.I. (Graphic User Interface) connected to a G.I.S. In a first stage, a Kohonen neural network is used to found clusters and membership functions, and then to compute a first set of fuzzy 'IF-THEN' rules with certainty factors. The human expert then updates these rules, and the given samples, according to his own experience. Once satisfying and discriminating classification rules are found, a second kind of neural network using back propagation is used to tune the final set of rules. At the same time, it produces neural nets able to give for each pixel and each class, the realisation degree of the favourable context relatively to the knowledge inferred by the samples.

Paper Details

Date Published: 30 December 1994
PDF: 12 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196766
Show Author Affiliations
Laurent Mascarilla, Univ. Paul Sabatier (France)


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

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