Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms

Cuckoo search based multi-level thresholding is presented by maximizing the Tsallis entropy.Different optimization algorithms are exploited with Tsallis entropy method.Cuckoo based Tsallis entropy was found to be more accurate for colored satellite image segmentation.The feasibility of the proposed approach has been tested on 10 different colored satellite images. In this paper, a new technique for color image segmentation using CS algorithm supported by Tsallis entropy for multilevel thresholding has been proposed toward the effective colored segmentation of satellite images. The nonextensive entropy is a new expansion in statistical mechanics, and it is a recent formalism in which a real quantity q was introduced as parameter for physical systems that presents the long range interactions, long time memories and fractal-type structures. The feasibility of the proposed cuckoo search and Tsallis entropy based approach was tested on 10 different satellite images and benchmarked with differential evolution, wind driven optimization, particle swarm optimization and artificial bee colony algorithm for solving the multilevel colored image thresholding problems. Experiments have been conducted on a variety of satellite images. Several measurements are used to evaluate the performance of proposed method which clearly illustrates the effectiveness and robustness of the proposed algorithm. The experimental results qualitative and quantitative both demonstrate that the proposed method selects the threshold values effectively and properly.

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