立方体是一个很好的形式:通过多维和高阶结构的超光谱带选择保留了聚类
Xiaogao Yang1, Deqiong Ding2, Fei Xia3
1College of Mechanical Engineering, Hunan University of Arts and Science, Changde 415000, China.
概括
本研究引入了一种新的多维高阶结构保存集群 (MHSPC) 方法,用于超光谱频段选择. 通过保留空间和光谱结构,MHSPC有效地减少了超光谱图像中的噪声和冗余.
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:
- 超光谱图像 (HSI) 含有丰富的光谱信息,但受到高维度和冗余的影响.
- 有效的超谱频段选择对于降低噪声和提高后续HSI分析任务的性能至关重要.
- 现有的方法往往忽略了HSI数据中固有的多维结构信息.
研究的目的:
- 提出一种新的超光谱带选择方法,MHSPC,有效地捕获多维结构信息.
- 利用张量分解和图形规则化来保护本地和全球数据结构.
- 通过选择一个紧和信息丰富的光谱频段子集来提高HSI分析的性能.
主要方法:
- 将HSI数据表示为张量立方体,并应用张量CP分解来提取隐藏的特征.
- 整合一个图形调节器,以保持沿光谱维度的局部几何结构.
- 使用核规范约束来捕获基于HSI低级属性的全球数据结构信息.
- 开发一个高效的交替更新算法,并提供融合保证.
主要成果:
- 拟议的MHSPC方法有效地保留了HSI数据中的空间和光谱结构.
- 在四个基准数据集中,MHSPC在超频谱带选择任务中超越了最先进的方法.
- 该方法成功地捕获了本地和全球数据结构,与传统的基于矢量方法不同.
结论:
- 通过考虑多维结构,MHSPC提供了一种优越的超频谱带选择方法.
- 该方法为减少HSI数据中的噪音和冗余提供了有效的策略.
- 通过优化频段选择,MHSPC显示了通过优化频段选择来改进各种HSI应用的巨大潜力.
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