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.
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