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Segmentation of remote sensing images for building detection

This paper presents a novel segmentation algorithm based on optimizing histogram multi-level thresholding of images by employing a variation of particle swarm optimization (PSO) Algorithm which improves the accuracy and the speed of segmentation based on the conventional PSO algorithm. Entropy has been chosen as the criteria for segmentation based on the multi-level thresholding. Entropy is input parameter of a fitness function for finding the best segmentation level. We have to find the optimum thresholding level based on the entropy of different image segments. A new optimization algorithm that called Hybrid cooperative- comprehensive learning PSO (HCOCLPSO), is used for optimization in this paper. This algorithm overcomes on common problems of basic variants of PSO, which are curse of dimensionality and tendency of premature convergence or in other word, getting stuck in local optima. This segmentation technique has been compared with conventional segmentation based on PSO and genetic algorithm (GA). We presented our segmentation results to experts. Our subjective measurements by experts show that we can achieve about 80 percents accuracy which is a better result when compared with conventional PSO and genetic algorithm. In terms of seed we can achieve much higher performance than two other schemes.