Hybrid grasshopper optimization algorithm and differential evolution for multilevel satellite image segmentation

Heming Jia, Chunbo Lang, Diego Oliva, Wenlong Song, Xiaoxu Peng

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


An effcient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA-jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search effciency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon's rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

Original languageEnglish
Article number1134
JournalRemote Sensing
Issue number9
Publication statusPublished - 1 May 2019
Externally publishedYes


  • Differential evolution
  • Grasshopper optimization algorithm
  • Hybrid optimization
  • Image segmentation
  • Image thresholding
  • Minimum cross entropy
  • Satellite image

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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