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

Lorenzo Bruzzone; Diego Fernandez-Prieto

doi:10.1117/12.331876

4 December 1998 Classification of remote sensing images using radial-basis-function neural networks: a supervised training technique

Lorenzo Bruzzone, Diego Fernandez-Prieto

Author Affiliations +

Proceedings Volume 3500, Image and Signal Processing for Remote Sensing IV; (1998) https://doi.org/10.1117/12.331876
Event: Remote Sensing, 1998, Barcelona, Spain

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.

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Lorenzo Bruzzone and Diego Fernandez-Prieto "Classification of remote sensing images using radial-basis-function neural networks: a supervised training technique", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331876

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