Abstract
In comparison with the Kohonen neural networks, the structure of the compete study networks is relatively simple because it does not consider neighboring neural units. In experiments, the authors adopted this simplified neural structure and improved its study algorithm by using max-min distance means. Experimental results show that the classification accuracy and efficiency of the improved compete study networks are remarkably raised in unsupervised classification of remote sensing imagery, and hence the technique of compete study networks has the practical application value.
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