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Proceedings Paper

Gaussian mixtures versus MLP for terrain classification in Landsat TM images
Author(s): Jose L. Alba Castro; Laura Docio; Domingo Docampo
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Paper Abstract

In this paper we introduce a new technique to build a Gaussian mixture classifier. It is based on the selection of the number and location of nodes dedicated to every class by means of discriminative rules. This feature allows us to make a fair comparison with MLP networks for terrain classification in remote sense applications, a field where non-parametric techniques usually outperform classical ML Gaussian classifiers. The main characteristic of the architecture proposed is the ability to select the proper number of Gaussian nodes per class attending to discriminative rules. The growth control is imposed by the use of an information theoretic criterion that prevents the network from becoming extremely complex, thus loosing generalization capabilities. After the growing phase is finished, a mutual information criterion is maximized to bias the parameters to a more discriminative configuration. We report a comparative study on terrain classification over a Landsat-TM image, using this technique and MLP classifiers with one hidden layer.

Paper Details

Date Published: 4 April 1997
PDF: 11 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271523
Show Author Affiliations
Jose L. Alba Castro, Univ. de Vigo (Spain)
Laura Docio, Univ. de Vigo (Spain)
Domingo Docampo, Univ. de Vigo (Spain)

Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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