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Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

1
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
2
Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara C.P. 44100, Jalisco, Mexico
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1421; https://doi.org/10.3390/rs11121421
Received: 2 May 2019 / Revised: 10 June 2019 / Accepted: 12 June 2019 / Published: 14 June 2019
(This article belongs to the Special Issue Image Optimization in Remote Sensing)
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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. View Full-Text
Keywords: satellite image; thresholding; image segmentation; Harris hawks optimization; mutation mechanism; Kapur’s entropy satellite image; thresholding; image segmentation; Harris hawks optimization; mutation mechanism; Kapur’s entropy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Jia, H.; Lang, C.; Oliva, D.; Song, W.; Peng, X. Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation. Remote Sens. 2019, 11, 1421.

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