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Multimedia Tools and Applications

, Volume 77, Issue 24, pp 31787–31805 | Cite as

A fast SAR image segmentation method based on improved chicken swarm optimization algorithm

  • Jianhui Liang
  • Lifang Wang
  • Miao Ma
  • Jian Zhang
Article
  • 51 Downloads

Abstract

Severe speckle noise existed in synthetic aperture radar (SAR) image presents a challenge to image segmentation. Though some traditional segmentation methods for SAR image have some success, most of them fail to consider segmentation effects and segmentation speed at the same time. In this paper, we propose a novel method of SAR image fast segmentation which is based on an improved chicken swarm optimization algorithm. In this method, the positions of the whole chicken swarm are firstly initialized in a narrowed foraging space. Secondly, the grey entropy model is selected as the fitness function of the improved chicken swarm optimization algorithm. Hence, the optimal threshold value is located gradually and quickly by virtue of the foraging behaviors of chicken swarm with a hierarchal order. Experimental results show that our method is superior to some segmentation methods based on genetic algorithm, artificial fish swarm algorithm in convergence, stability and segmentation effects.

Keywords

Image segmentation Chicken swarm optimization algorithm Swarm intelligence SAR image 

Notes

Acknowledgments

This work is supported by Hainan Provincial Natural Science Foundation of China(618QN220), the Education and Teaching Reform Research object of Hainan University of China(hdjy1730), the Agricultural Science and Technology Innovation and Public Relations project of Shaanxi Province of China (2016NY-176), the Fundamental Research Funds for the Central Universities of Shaanxi Normal University (GK201703054, GK201603083, GK201703058), the Key Science and Technology Innovation Team in Shaanxi Province of China(2014KTC-18) and the National Natural Science Foundation of China (61373120).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceNorthwestern Polytechnical UniversityXi’ anChina
  2. 2.Institute of Tropical Agriculture and ForestryHaiNan UniversityDan zhouChina
  3. 3.School of Computer ScienceShaanxi Normal UniversityXi’ anChina

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