Neural-fuzzy models for multispectral image analysis

In this paper, we consider neural-fuzzy models for multispectral image analysis. We consider both supervised and unsupervised classification. The model for supervised classification consists of six layers. The first three layers map the input variables to fuzzy set membership functions. The last three layers implement the decision rules. The model learns decision rules using a supervised gradient descent procedure. The model for unsupervised classification consists of two layers. The algorithm is similar to competitive learning. However, here, for each input sample, membership functions of output categories are used to update weights. Input vectors are normalized, and Euclidean distance is used as the similarity measure. In this model if the input vector does not satisfy the "similarity criterion," a new cluster is created; otherwise, the weights corresponding to the winner unit are updated using the fuzzy membership values of the output categories. We have developed software for these models. As an illustration, the models are used to analyze multispectral images.