Abstract
In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.
Highlights
Image segmentation is a fundamental and crucial stage in some applications, such as computer vision, pattern recognition, image classification, etc. [1,2,3,4]
Kapur’s entropy and Otsu’s method are used to select the segmentation thresholds. It can be found from the results that the proposed approach can accurately determine the segmentation thresholds and significantly reduce the computational complexity
For the third part of the experiment, DHHO/M is compared with other thresholding techniques based on different criteria to illustrate its feasibility and universality, such as Tsallis entropy based modified grasshopper optimization algorithm (MGOA) [20] and modified artificial bee colony (MABC) [13], as well as Otsu method based modified flower pollination algorithm (MFPA) [48] and grey wolf optimizer (GWO) [7]
Summary
Image segmentation is a fundamental and crucial stage in some applications, such as computer vision, pattern recognition, image classification, etc. [1,2,3,4]. According to [28], HHO has presented better or occasionally competitive results than well-established techniques on 29 benchmark problems and several real-world engineering tasks These phenomena illustrate the potential and the superiority of HHO, which motivates us to apply this powerful algorithm to multilevel image thresholding. The influence of parameter change on the performance of disturbance term, and the reason for using the Gaussian distribution instead of the Levy distribution is analyzed As for the latter, a famous mutation operator, known as DE/best/2 is utilized to improve the global search efficiency and population diversity [29]. The conclusion and future research direction are represented in the last section
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