🤖 AI Summary
This work addresses the limitations of existing hyperspectral band selection methods, which are sensitive to initialization, require a preset number of bands lacking flexibility, and exhibit unstable performance under spatially disjoint evaluation protocols. To overcome these issues, the authors propose SGBR-HC, a two-stage approach that first performs supervised band ranking based on class separability and spectral diversity to provide an informative prior for learnable sparse gating. In the second stage, the sparse gating module and a spatial classifier are jointly trained to adaptively determine the optimal number of bands. By integrating differentiable sparse gating, Hard-Concrete initialization, and spatially disjoint evaluation, the method effectively prevents information leakage. Experiments on the Pavia University and Houston 2013 datasets demonstrate state-of-the-art overall accuracy and Cohen’s kappa using only approximately 20 selected bands, while ablation studies confirm the critical role of the ranking prior.
📝 Abstract
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.