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使用光谱空间代贪算法进行无监督的高光谱带选择

Xin Yang¹, Wenhong Wang¹

¹College of Computer Science, Liaocheng University, Liaocheng 252000, China.

Sensors (Basel, Switzerland)

|September 27, 2025

概括

超光谱带选择 (BS) 通过新的光谱空间代贪算法 (SSIGA) 得到了改进. 该方法有效地使用空间和光谱信息,以更好地减少超光谱遥感图像 (HSI) 中的数据维度.

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:

超光谱带选择 (BS) 对于降低超光谱遥感图像 (HSI) 中的数据维度至关重要.
现有的基于搜索的BS方法往往无法充分利用固有的空间和光谱信息,从而限制了它们的有效性.
需要无监督的BS方法来整合空间和光谱的先前信息.

研究的目的:

提出一种新的无监督频段选择方法,即光谱空间代贪算法 (SSIGA).
解决现有方法的局限性,有效地利用高质量信息系统中的空间和光谱先前信息.
为了提高BS的性能,用于HSI的分类应用.

主要方法:

对于光谱信息处理,SSIGA采用K-means集群与平衡的集群大小约束.
每个集群都构建了一个K-最近邻近图,以促进有效的本地搜索.
一个客观函数通过使用费舍尔分数,超像素细分,信息和相互信息来评估带的可区分性和冗余性.

主要成果:

与最先进的方法相比,SSIGA在三个真实HSI数据集上表现出优越的性能.
该算法有效地利用空间和光谱信息进行频段选择.
实验结果验证了拟议的目标功能和本地搜索策略的有效性.

结论:

拟议的SSIGA是HSI有效的无监督频段选择方法.
通过整合光谱和空间信息,SSIGA实现了卓越的性能.
该方法为高光谱图像分类中的维度减小提供了有希望的方法.

关键词:

频段的选择频段的选择超光谱遥感图像的使用代的贪算法代的贪算法当地搜索本地搜索频谱空间信息频谱空间信息

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