This paper presents multispectral space image classification using the new neural model, called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of small modular self-organizing neural networks. The performance of this classifier is compared with the performance of Bayesian classifiers. The implemented neural/statistical classifiers are evaluated using a LANDSAT TM image with 7 bands composed by a set of 7-dimensional pixels, a subset of which contains labelled pixels corresponding to 7 thematic categories. The best experimental result leads to a recognition rate of 95.29%. The model has potential applications for harbour protection.


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    A New Neural Approach for Pattern Recognition in Space Imagery

    Neagoe, V.-E. / Ropot, A.-D. | British Library Conference Proceedings | 2009




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