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Unsupervised classification approaches, also known as “clustering algorithms”, can be considered a solution to problems associated with the supervised classification of remotely sensed image data. The most important of these problems with respect to statistical classification algorithms is the lack of enough high quality training data and high dimensionality of hyperspectral data. In this paper, an improved clustering framework is developed and evaluated as a resolution to these problems. The proposed method enhances the Fuzzy C-Means (FCM) algorithm by using the Support Vector Domain Description (SVDD). The proposed algorithm operates in a similar manner as the FCM for the clustering and labeling of data vectors. However, for estimation of the cluster centers, the SVDD encircles the corresponding members and estimates the center of a containing sphere. By doing so, the effects of noise and outliers on the cluster centers are reduced, and more specifically, higher classification accuracy can be obtained. In spite of this advantage, there are two sets of parameters, namely, the SVDD's and FCM's parameters, both of which affect the performance of the proposed algorithm. Accordingly, the effects of these parameters and their optimum values have been evaluated as well. The evaluations of the results of experiments show that the proposed algorithm, due to the use of the SVDD algorithm, is more efficient than other clustering algorithms.