用模型解释指导选波段，30个波段就能保持高精度
Explainability-Driven Dimensionality Reduction for Hyperspectral Imaging
- 用解释方法量化每个波段对分类的贡献
- 仅用30个波段就达到或超过全谱精度
- 选中的波段对应物理上有意义的光谱区
高光谱成像（HSI）提供丰富的光谱信息，可用于精确的材料分类与分析；然而其高维特性带来计算负担和冗余，降维至关重要。本文探索在模型驱动框架中应用事后可解释性方法进行波段选择，以降低光谱维度并保留预测性能。通过解释已训练分类器，量化各波段对其决策的贡献。随后进行删除-插入评估，记录按重要性排序的波段被移除或重新引入时置信度的变化，并聚合为影响得分。选择影响得分最高的波段，得到紧凑的光谱子集，既保持准确率又提升效率。在两个公开基准（Pavia University 和 Salinas）上的实验表明，使用最少30个选定波段的分类器能匹配或超越全谱基线，同时显著降低计算需求。所得子集与物理上具有区分性的波长区域一致，表明基于模型对齐、解释引导的波段选择是高光谱成像有效降维的合理路径。
原文摘要 · Abstract (English)
Hyperspectral imaging (HSI) provides rich spectral information for precise material classification and analysis; however, its high dimensionality introduces a computational burden and redundancy, making dimensionality reduction essential. We present an exploratory study into the application of post-hoc explainability methods in a model--driven framework for band selection, which reduces the spectral dimension while preserving predictive performance. A trained classifier is probed with explanations to quantify each band's contribution to its decisions. We then perform deletion--insertion evaluations, recording confidence changes as ranked bands are removed or reintroduced, and aggregate these signals into influence scores. Selecting the highest--influence bands yields compact spectral subsets that maintain accuracy and improve efficiency. Experiments on two public benchmarks (Pavia University and Salinas) demonstrate that classifiers trained on as few as 30 selected bands match or exceed full--spectrum baselines while reducing computational requirements. The resulting subsets align with physically meaningful, highly discriminative wavelength regions, indicating that model--aligned, explanation-guided band selection is a principled route to effective dimensionality reduction for HSI.
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