Abstract
A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon's rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.
Highlights
With the booming of artificial intelligence (IA) technology, in order to meet people’s needs, the practicality of computer vision technology is highly emphasized
This paper extensively studies the improved algorithm for color satellite image segmentation based on multilevel threshold
In order to solve the problem of a large amount of information and high precision of satellite image segmentation, a method combining the improved TLMVO algorithm with the much-anticipated Masi entropy in recent years is adopted
Summary
With the booming of artificial intelligence (IA) technology, in order to meet people’s needs, the practicality of computer vision technology is highly emphasized. Image segmentation is one of the main problems of digital image processing technology and machine vision technology [1], which can be either gray image segmentation or color image segmentation. Color images contain more color information such as hue and saturation [2,3]. Images have a wide range of applications in the fields of geographic graphic information systems, astronomy and earth science research. It is necessary to locate objects and boundaries accurately in satellite images. Color satellite image segmentation is a critical and challenging topic [4,5,6]
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