Abstract
The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons with conventional FCM clustering technique and Bayesian classification technique are also presented. Second, we present a two-step classifier in which the proposed SFCM and Bayesian algorithms are used in a cooperative way such that classification results of the SFCM algorithm are used to compute the prior probabilities required for the Bayesian classifier. Classification results of the three algorithms are presented on simulated and real remote sensing multispectral data. The results show improvement in the classification accuracy and reliability using the two-step algorithm.