Crisp and fuzzy competitive learning networks for supervised classification of multispectral IRS scenes

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Crisp and fuzzy competitive learning network schemes have been designed for classification of multispectral IRS-1B satellite images. For supervised learning, an extension of competitive learning network with a Grossberg layer, sometimes known as a 'forward only' Counter-propagation Network (CPN) has been used. The 'concept of winner' of a classical Kohonen's network has been fuzzified in this model. This model is found to yield much better accuracy than the crisp Kohonen's network and marginally better accuracy than the Maximum Likelihood Classifier. The results are discussed.