Multilevel thresholding Aerial image segmentation using comprehensive learning-based Snow ablation optimizer with double attractors

Authors

  • Mohamed Abd Elaziz Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt Faculty of Computer Science and Engineering, Galala University, Suze 435611, Egypt Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon MEU Research Unit, Middle East University, Amman 11831, Jordan Author https://orcid.org/0000-0002-7682-6269
  • Mohammed A.A. Al-qaness Emirates International University image/svg+xml Author
  • Rehab Ali Ibrahim Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt Author
  • Ahmed A. Ewees Department of Computer, Damietta University, Damietta 34517, Egypt Author
  • Mansour Shrahili Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia Corresponding author at: Department of Computer, Damietta University, Damietta 34517, Egypt. Author https://orcid.org/0000-0003-3456-8393

DOI:

https://doi.org/10.1016/j.eij.2024.100500

Keywords:

Aerial image segmentation , Snow ablation optimizer , Comprehensive learning , Double attractors

Abstract

Aerial photography is a remote sensing technique used for target detection, enabling both qualitative and quantitative analysis. The segmentation process is considered one of the most important processes to improve the analysis of Aerial images. In this study, we introduce an alternative multilevel threshold image segmentation based on a modified Snow ablation optimizer (SAO) algorithm. This modification is conducted using the strengths of Comprehensive learning and Double attractors which aims to enhance the exploration and exploitation abilities of the SAO during the process of discovering the optimal threshold levels that are used to segment the Aerial photography image. To validate the quality of the modified version of SAO, named DCSAO, a set of experimental series is conducted using the CEC2022 benchmark function and sixteen Aerial images at different threshold levels. In addition, we compared the results of DCSAO with different well-known Metaheuristic techniques. The results show the superior performance of DCSAO in comparison to other algorithms according to the performance metrics.

Author Biography

  • Mohammed A.A. Al-qaness, Emirates International University
    Mohammed A.A. Al-qaness

    View in Scopus

    College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China   Zhejiang Institute of Optoelectronics, Jinhua 321004, China   College of Engineering and Information Technology, Emirates International University, Sana’a 16881, Yemen   Corresponding author at: College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China. alqaness@zjnu.edu.cn

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How to Cite

Abd Elaziz, M., Al-qaness, M. A., Ibrahim, R. A., Ewees, A. A., & Shrahili, M. (2024). Multilevel thresholding Aerial image segmentation using comprehensive learning-based Snow ablation optimizer with double attractors. Emirates International University Digital Repository, 1(1). https://doi.org/10.1016/j.eij.2024.100500

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