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4 April 1997 Gaussian mixtures versus MLP for terrain classification in Landsat TM images
Jose L. Alba Castro, Laura Docio, Domingo Docampo
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Proceedings Volume 3077, Applications and Science of Artificial Neural Networks III; (1997) https://doi.org/10.1117/12.271523
Event: AeroSense '97, 1997, Orlando, FL, United States
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.
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Jose L. Alba Castro, Laura Docio, and Domingo Docampo "Gaussian mixtures versus MLP for terrain classification in Landsat TM images", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271523
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KEYWORDS
Earth observing sensors
Image classification
Landsat
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