一种有效的特征选择方法,基于混合灰狼优化器和基因算法,用于高光谱图像
Yiqun Shang1,2, Minrui Zheng3, Jiayang Li4
1School of Information Engineering, China University of Geosciences, Beijing, 100083, China.
Scientific reports
|January 14, 2025
概括
一个新的混合算法,灰狼优化器和遗传算法 (GWOGA),通过平衡探索和利用来提高高频谱图像分类,以有效选择特征.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 特征选择 (FS) 对于高光谱图像 (HSI) 分类至关重要,减少维度,同时保持准确性.
- 现有的群体智能和进化算法 (SIEA) 在高维HSI中与探索和局部最佳情况作斗争.
研究的目的:
- 开发一种新的混合算法,GWOGA,用于高光谱图像分类中的有效特征选择.
- 在特征选择算法中增强勘探和开发之间的平衡.
主要方法:
- 拟议的GWOGA算法结合了灰狼优化器 (GWO) 和遗传算法 (GA).
- 纳入混乱地图和基于对立的学习 (OBL) 用于人口初始化以改善多样性.
- 实施精英学习策略和混合优化机制,以实现高效的搜索和局部最佳避免.
主要成果:
- 与基准HSI (印度松树,KSC,博茨瓦纳) 的最先进算法相比,GWOGA表现优越.
- 通过减少所选频段的数量,实现了更高的分类准确性.
- 验证了拟议的GWOGA算法的稳定性和通用性.
结论:
- 在高光谱图像分类中,GWOGA为特征选择提供了有效的解决方案.
- 该算法的混合方法成功地解决了现有的SIEAs的局限性.
- 在HSI数据分析中,GWOGA显示了实践应用的巨大潜力.
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