Reliability-Aware Adaptive Band Gating with Domain Expansion for Cross-Scene Hyperspectral Band Selection
TL;DR
Together, these results demonstrate that ABG learns compact and traceable original-band subsets with strong downstream transfer utility across the evaluated cross-scene settings.
Abstract
Cross-scene hyperspectral band selection must reduce spectral redundancy while retaining channels that remain useful beyond the source scene. We propose Adaptive Band Gating (ABG), a source-only selector that combines frequency-domain decoupling enhancement, global and sample-specific gating, source-side spectral, spatial, morphology-inspired, and sensor-noise perturbations, and a dual-head evaluator. The selector is trained with source data and frozen before downstream evaluation. Selected bands are assessed with a radial-basis-function support vector machine on Pavia Center and HyRANK under fixed band budgets and target-label fractions from 0% to 10%. At 5% target labels, 15 selected bands achieve 96.05% overall accuracy on Pavia Center, compared with 95.41% using all 102 bands; 20 selected bands achieve 81.49% on HyRANK, compared with 79.20% using all 176 bands. Across evaluated band budgets, ABG is comparable to XGBS on Pavia Center and provides stronger results on HyRANK. Ablation experiments show that adaptive gating, frequency-domain enhancement, and source-side expansion each contribute to performance. Together, these results demonstrate that ABG learns compact and traceable original-band subsets with strong downstream transfer utility across the evaluated cross-scene settings.