An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation

In this article a multiobjective technique using improved differential evolution for fuzzy clustering has been proposed that optimizes multiple validity measures simultaneously. The resultant set of near-pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements. Real-coded encoding of the cluster centres is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. One satellite image has also been classified using the proposed technique to establish its efficiency.