A New Adaptive Fuzzy Clustering Algorithm for Remotely Sensed Images

This paper introduces a new adaptive fuzzy clustering algorithm which combines the capability of fuzzy mathematics and adaptation. This adaptive capability is achieved by using the mechanism of splitting and merging. Unlike most of the fuzzy clustering algorithms which require a priori knowledge about the number of classes in the dataset, this new algorithm can learn the number of classes dynamically. It also gives the higher accuracy of clustering results with fuzzy mathematics. A comparison with the K-Means, ISODATA, Fuzzy C-Means and Possibilistic C-Means shows that the algorithm is effective in image segmentation. The algorithm also enhances the adaptive capability of the ISODATA.