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使用基于多策略Osprey优化算法的多级值进行空中图像细分

Mohamed Abd Elaziz^1,2, Mohammed Azmi Al-Betar^3,4, Ahmed A Ewees⁵

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

Scientific reports

|March 17, 2026

概括

本研究介绍了一种修改后的 Osprey 优化算法 (MOOA),用于空中图像分割. 通过优化多级值,MOOA提高了图像细分质量,提高了远程传感应用的准确性.

科学领域:

计算机视觉计算机视觉
遥感遥感遥感遥感
图像处理图像处理

背景情况:

空中图像细分对于各种应用,包括城市规划,环境监测和灾害管理至关重要.
精确的细分依赖于有效的值技术,从空中图像中提取有意义的信息.
现有的优化算法可能遭受过早的融合,限制了细分性能.

研究的目的:

开发一个先进的多级值技术用于空中图像分割.
通过使用多策略机制来提高Osprey优化算法 (OOA) 的性能.
评估拟议的修改的 Osprey 优化算法 (MOOA) 在细分空中图像中的有效性.

主要方法:

一个修改后的 Osprey 优化算法 (MOOA) 被开发出来,它包含了双重吸引器,用于增强的探索和动态随机搜索,用于改进的开发.
MOOA中的多策略机制旨在防止过早的融合,并提高整体绩效.
MOOA应用于航空图像分割的多级值,并使用16张航空图像进行验证.

主要成果:

MOOA在确定最佳值方面表现出很高的能力,显著提高了细分图像质量.
性能指标,如峰值信号与噪声比率 (PSNR),特征相似性指数测量 (FSIM) 和结构相似性指数测量 (SSIM) 显示了显著的改善.
与已建立的多级值方法进行比较分析证实了MOOA的优越性.

结论:

拟议的MOOA有效地解决了标准OOA的局限性,在空中图像细分方面提供了卓越的性能.
开发的技术为从航空图像中提取准确信息提供了强大的解决方案.
MOOA代表了计算机视觉和遥感任务的有希望的进步,这些任务需要精确的图像细分.

关键词:

空中图像细分的细分方法双重的吸引器是双重的吸引器.多策略机制多策略机制.多级值设置多级值设置 Osprey 的优化算法 (OOA)

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