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Aerial image segmentation using multilevel thresholding based on multi strategy Osprey optimization algorithm.

Mohamed Abd Elaziz1,2, Mohammed Azmi Al-Betar3,4, Ahmed A Ewees5

  • 1Department of Mathematics, Faculty of Science, Zagazig University, Zagazig , 14459, Egypt. abd_el_aziz_m@yahoo.com.

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Summary
This summary is machine-generated.

This study introduces a modified Osprey Optimization Algorithm (MOOA) for aerial image segmentation. The MOOA enhances image segmentation quality by optimizing multilevel thresholding, improving accuracy in remote sensing applications.

Keywords:
Aerial image segmentationDouble attractorsMulti-strategy mechanismMultilevel thresholdingOsprey optimization algorithm (OOA)

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Image Processing

Background:

  • Aerial image segmentation is crucial for diverse applications including urban planning, environmental monitoring, and disaster management.
  • Accurate segmentation relies on effective thresholding techniques to extract meaningful information from aerial imagery.
  • Existing optimization algorithms may suffer from premature convergence, limiting segmentation performance.

Purpose of the Study:

  • To develop an advanced multilevel thresholding technique for aerial image segmentation.
  • To enhance the Osprey Optimization Algorithm (OOA) using a multi-strategy mechanism for improved performance.
  • To evaluate the efficacy of the proposed Modified Osprey Optimization Algorithm (MOOA) in segmenting aerial images.

Main Methods:

  • A modified Osprey Optimization Algorithm (MOOA) was developed incorporating double attractors for enhanced exploration and a dynamic random search for improved exploitation.
  • The multi-strategy mechanism in MOOA aims to prevent premature convergence and boost overall performance.
  • MOOA was applied to multilevel thresholding for aerial image segmentation and validated using sixteen aerial images.

Main Results:

  • The MOOA demonstrated a high capability in determining optimal threshold values, significantly improving segmented image quality.
  • Performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Feature Similarity Index Measure (FSIM), and Structural Similarity Index Measure (SSIM) showed substantial improvements.
  • Comparative analysis against established multilevel thresholding methods confirmed the superiority of the MOOA.

Conclusions:

  • The proposed MOOA effectively addresses the limitations of standard OOA, offering superior performance in aerial image segmentation.
  • The developed technique provides a robust solution for accurate information extraction from aerial imagery.
  • MOOA represents a promising advancement for computer vision and remote sensing tasks requiring precise image segmentation.