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Proceedings Paper

Comparison of neural networks and classical texture analysis
Author(s): David Blacknell; Richard Geoffrey White
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Paper Abstract

In this paper, it is investigated how closely neural networks can approach the optimum classification of radar textures. To this end, a factorization technique is presented which aids convergence to the best possible solution obtainable from the training data. This factorization scheme is designed to be fully general. The specific performances of the factorized networks are studied, in this radar clutter classification problem, when applied to uncorrelated K distributed images. These results are then compared with the maximum likelihood performance and the performances of various intuitive and approximate classification schemes. Furthermore, preliminary network results are presented for the classification of correlated processes and these results are also compared to results obtained using classical techniques.

Paper Details

Date Published: 2 March 1994
PDF: 7 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169968
Show Author Affiliations
David Blacknell, Defence Research Agency Malvern (United Kingdom)
Richard Geoffrey White, Defence Research Agency Malvern (United Kingdom)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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