🤖 AI Summary
Hyperspectral imaging (HSI) suffers from high spectral dimensionality and substantial redundancy, necessitating dimensionality reduction methods that jointly optimize predictive performance and interpretability. This paper proposes a post-hoc interpretable band selection framework for HSI: it introduces the deletion-insertion evaluation paradigm to HSI band selection for the first time, quantifying each band’s influence score on a pre-trained classifier’s decision. High-impact bands with clear physical meaning are then selected. The method integrates model-aligned contribution analysis, influence score aggregation, and empirical validation. Experiments on the Pavia University and Salinas datasets demonstrate that classifiers using only 30 selected bands achieve accuracy comparable to or exceeding that of full-spectrum models, while significantly reducing computational cost. Crucially, the approach ensures both interpretability—through transparent, physics-grounded band selection—and generalizability across diverse scenes and classifiers.
📝 Abstract
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.