  This paper proposes an OTSU based differential evolution method for satellite
image segmentation and compares it with four other methods such as Modified
Artificial Bee Colony Optimizer (MABC), Artificial Bee Colony (ABC), Genetic
Algorithm (GA), and Particle Swarm Optimization (PSO) using the objective
function proposed by Otsu for optimal multilevel thresholding. The experiments
conducted and their results illustrate that our proposed DE and OTSU algorithm
segmentation can effectively and precisely segment the input image, close to
results obtained by the other methods. In the proposed DE and OTSU algorithm,
instead of passing the fitness function variables, the entire image is passed
as an input to the DE algorithm after obtaining the threshold values for the
input number of levels in the OTSU algorithm. The image segmentation results
are obtained after learning about the image instead of learning about the
fitness variables. In comparison to other segmentation methods examined, the
proposed DE and OTSU algorithm yields promising results with minimized
computational time compared to some algorithms.
