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Clustering of Remote Sensing Data Based on Spherical Evolution Algorithm

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Many meta-heuristic algorithms have been used in many areas, e.g., pattern recognition, machine learning, information retrieval, data mining, and image analysis. These algorithms have become powerful and popular in image clustering. In this study, a Spherical Evolution Algorithm (SEA) is applied during image clustering. The SE adopts a novel spherical search mechanism instead of the conventional hypercube search mechanism. First, a synthetic image is classified using the spherical evolution algorithm by varying the Scale Factor (SF) and the number of the function evaluations (FES) parameters. The best values of these parameters obtained by the first classification will be used for the classification of the real satellite image (Landsat TM imagery). Secondly, SEA and other meta-heuristic algorithms as the Genetic Algorithm (GA), the Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) are implemented and applied on real image. The comparison results demonstrate that SEA approach outperforms other methods in terms of execution times and global minimum values.
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Spherical Evolution Algorithm; Meta-Heuristic Algorithms; Image Clustering; Remote Sensing

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