Rényi’s Entropy and Bat Algorithm Based Color Image Multilevel Thresholding

Colored satellite images are difficult to segment due to their low illumination, dense features, uncertainties, etc. Renyi’s entropy is a famous entropy criterion that provides excellent outputs in bi-level thresholding based segmentation. But such method suffers lack of accuracy, inefficiency, and instability when extended to perform color image multilevel thresholding. Therefore, a new color image multilevel segmentation strategy based on Bat algorithm and Renyi’s entropy is proposed in this paper to determine the optimal threshold values more efficiently. The experiments are conducted on four real satellite images and two well-known test images at different threshold levels. The study shows that the proposed algorithm obtains good quality and adequate segmented results more effectively as compared to other multilevel thresholding algorithms such as Renyi’s-PSO and Otsu-PSO.

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