面向空间分离评估的高光谱分类自适应波段选择
Adaptive Band Selection for Hyperspectral Classification with Spatially Disjoint Evaluation
AI总结：
提出SGBR-HC方法，通过监督光谱排序初始化可训练稀疏门，自适应确定波段数，在空间分离评估下以约20个波段取得最高平均总体精度和Kappa系数。
摘要：
基于可微选择器的高光谱波段选择方法可能对初始化和提取最终离散子集敏感，而预设的波段数量限制了灵活性。我们提出SGBR-HC（光谱组波段排序与硬混凝土初始化），一种两阶段方法，使用监督光谱排序来初始化可训练稀疏门，而不是将排序视为固定选择规则，让所选波段的数量由训练决定。第一阶段通过类别可分性和光谱多样性对训练像素的候选波段进行评分；该排序为第二阶段的门控逻辑值提供种子，第二阶段将稀疏门与空间分类器联合训练。在帕维亚大学和休斯顿2013数据集上进行空间分离评估，并通过在所选波段上重新训练新分类器进行验证，SGBR-HC以大约20个波段实现了最高的平均总体精度和Cohen's kappa。跳过第一阶段导致帕维亚大学的OA下降8.84个百分点，休斯顿2013下降22.15个百分点，证实了排序先验的作用。随机像素分割使帕维亚大学的OA膨胀30.56个百分点，强调了空间泄漏作为关键评估混淆因素。
英文摘要：
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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