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Proceedings Paper

Classification of remote sensing images using radial-basis-function neural networks: a supervised training technique
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Paper Abstract

A supervised technique for training Radial Basis Function (RBF) neural classifiers is proposed. Such a technique, unlike traditional ones, considers the class-memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The proposed method has significant advantages over traditional ones in terms of classification accuracy and stability of the network. Experimental results, carried out on a multisensor remote-sensing data set, confirm the validity of the proposed technique.

Paper Details

Date Published: 4 December 1998
PDF: 8 pages
Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); doi: 10.1117/12.331876
Show Author Affiliations
Lorenzo Bruzzone, Univ. of Genoa (Italy)
Diego Fernandez-Prieto, Univ. of Genoa (Italy)

Published in SPIE Proceedings Vol. 3500:
Image and Signal Processing for Remote Sensing IV
Sebastiano Bruno Serpico, Editor(s)

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