一个增强的Jaya算法与突变和多样性保护策略,用于高光谱带选择
Suchismita Behera1, Partha Pratim Sarangi1, Bhabani Shankar Prasad Mishra1
1School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India.
F1000Research
|November 6, 2025
概括
这项研究引入了一种改进的Jaya算法,用于高光谱频段选择,提高分类准确性和减少计算负载. 这种新的方法平衡了探索和利用,以实现最佳的高光谱图像处理.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 超光谱带的选择对于减少冗余性和改善超光谱图像 (HSI) 处理中的分类至关重要.
- 由于其组合性质,最优的频段选择在计算上具有挑战性.
- 现有的元启发算法很难有效地平衡探索和利用.
研究的目的:
- 为有效的高光谱频段选择提出一种新的元启发方法.
- 为了增强探索和保持在搜索空间的解决方案多样性.
- 为了同时优化分类性能和频段缩小.
主要方法:
- 一个无参数的二进制Jaya算法与突变运算符相结合,用于增强的探索.
- 基于对立的学习 (OBL) 用于人口初始化和准反射用于重新初始化以保持多样性.
- 一个加权总和的多目标健身功能,以最大限度地减少冗余并增强概括性.
主要成果:
- 拟议的方法在印度松树,帕维亚大学和萨利纳斯基准数据集上进行了评估.
- 实验结果显示,与现有的基于元启发的乐队选择技术相比,其性能优越.
- 这种方法有效地平衡了分类准确性和频段缩小.
结论:
- 新的Jaya算法与OBL和突变提供了高光谱带选择的有效解决方案.
- 该方法在分类性能和计算效率方面取得了显著的改进.
- 它非常适合各种高光谱成像应用.
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