提出自适应波段选择方法，用空间不重叠验证提升高光谱分类精度。
Adaptive Band Selection for Hyperspectral Classification with Spatially Disjoint Evaluation
- 两阶段设计：先按判别力与多样性评分候选波段，再训练稀疏门控
- 仅需约20个波段即达最高平均精度和科恩卡帕值
- 适合高光谱图像分类任务，尤其关注评估公平性研究者
基于可微选择器的高光谱波段选择方法易受初始化影响且固定波段数量限制灵活性。本文提出SGBR-HC（基于硬-混凝土初始化的光谱组波段排序），采用两阶段策略：第一阶段通过训练像素的类别判别力与光谱多样性对候选波段进行监督排序，作为第二阶段可训练稀疏门控的初始值；第二阶段联合训练稀疏门控与空间分类器，由训练过程自动确定最终选中波段数。在帕维亚大学和休斯顿2013数据集上采用空间不重叠评估，重新训练分类器验证所选波段性能，SGBR-HC取得最高平均精度与科恩卡帕值，仅需约20个波段。若跳过第一阶段，帕维亚大学平均精度下降8.84个百分点，休斯顿2013下降22.15个百分点，证实排序先验的重要性。随机像素划分使帕维亚大学平均精度虚增30.56个百分点，凸显空间泄漏是评估中的关键混淆因素。
原文摘要 · Abstract (English)
Hyperspectral band selection methods based on differentiable selectors can be sensitive to initialization and to extracting a final discrete subset, while prescribed band counts limit flexibility. We propose SGBR-HC (Spectral-Group Band Ranking with Hard-Concrete initialization), a two-stage method that uses a supervised spectral ranking to initialize trainable sparse gates rather than treating ranking as a fixed selection rule, letting the number of selected bands be determined by training. Stage-1 scores candidate bands from training pixels by class discriminability and spectral diversity; this ranking seeds the gate logits for Stage-2, which trains the sparse gates jointly with a spatial classifier. Under spatially disjoint evaluation on Pavia University and Houston 2013, verified by retraining a fresh classifier on the selected bands, SGBR-HC achieves the highest mean overall accuracy and Cohen's kappa with approximately twenty bands. Bypassing Stage-1 degrades OA by 8.84 pp on Pavia University and 22.15 pp on Houston 2013, confirming the ranking prior's role. Random pixel splits inflate OA on Pavia University by 30.56 pp, underscoring spatial leakage as a critical evaluation confound.
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