基于相关性筛选高光谱图像波段，减少冗余信息
Correlation-Based Band Selection for Hyperspectral Image Classification
- 用相关系数计算波段间平均相关性，识别依赖关系
- 通过阈值筛选低相关性波段，保留信息多样性的子集
- 在帕维亚和萨利纳斯数据集上表现优于传统方法
高光谱图像在多个光谱波段上提供了地物的丰富光谱信息，但数据量庞大，处理困难。由于相邻波段间高度相关，通常仅选取部分波段用于各类应用。本文提出一种基于相关性的波段选择方法，通过计算波段间的平均相关系数来分析其相互关系。随后，基于平均相关性与阈值策略，选出一组低互依赖性的波段，确保所选波段提供非冗余且多样化的信息。我们在两个标准基准数据集——帕维亚大学（Pavia University, PA）和萨利纳斯谷（Salinas Valley, SA）上评估该方法，专注于图像分类任务。实验结果表明，该方法在性能上可与现有主流波段选择方法相媲美。
原文摘要 · Abstract (English)
Hyperspectral images offer extensive spectral information about ground objects across multiple spectral bands. However, the large volume of data can pose challenges during processing. Typically, adjacent bands in hyperspectral data are highly correlated, leading to the use of only a few selected bands for various applications. In this work, we present a correlation-based band selection approach for hyperspectral image classification. Our approach calculates the average correlation between bands using correlation coefficients to identify the relationships among different bands. Afterward, we select a subset of bands by analyzing the average correlation and applying a threshold-based method. This allows us to isolate and retain bands that exhibit lower inter-band dependencies, ensuring that the selected bands provide diverse and non-redundant information. We evaluate our proposed approach on two standard benchmark datasets: Pavia University (PA) and Salinas Valley (SA), focusing on image classification tasks. The experimental results demonstrate that our method performs competitively with other standard band selection approaches.
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