Wild Horse Optimization for Satellite Images Segmentation

The use of satellite imagery has become an integral aspect in the planning of multiple domains that include disaster management and analysis of natural calamity images, snow cover mapping, smart city development, etc. Satellite images have now become an important area in remote sensing studies. The nature inspired optimization algorithms are very promising with image segmentation techniques to provide a platform for processing of satellite images. In this paper a new satellite image segmentation method is developed using Wild Horse Optimization (WHO) to cluster the image into disjoint regions based on color information. Experiments performed on two different satellite images confirm the stability, homogeneity, and the efficiency of proposed method with comparison to K-means and Shuffled Frog Leaping Algorithm (SFLA).