Comparison of neural networks and classical texture analysis

David Blacknell; Richard Geoffrey White

doi:10.1117/12.169968

2 March 1994 Comparison of neural networks and classical texture analysis

David Blacknell, Richard Geoffrey White

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169968
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

David Blacknell and Richard Geoffrey White "Comparison of neural networks and classical texture analysis", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169968

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