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SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

Author

Listed:
  • M. Q. Li
  • L. P. Xu
  • Na Xu
  • Tao Huang
  • Bo Yan

Abstract

An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art.

Suggested Citation

  • M. Q. Li & L. P. Xu & Na Xu & Tao Huang & Bo Yan, 2018. "SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, August.
    • Handle: RePEc:hin:jnlmpe:4576015
    DOI: 10.1155/2018/4576015
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