Beyond Distribution Shifts: Adaptive Hyperspectral Image Classification at Test Time

Hyperspectral image (HSI) classification models are highly sensitive to distribution shifts caused by various real-world degradations such as noise, blur, compression, and atmospheric effects. To address this challenge, we propose HyperTTA, a unified framework designed to enhance model robustness under diverse degradation conditions. Specifically, we first construct a multi-degradation hyperspectral dataset that systematically simulates nine representative types of degradations, providing a comprehensive benchmark for robust classification evaluation. Based on this, we design a spectral-spatial transformer classifier (SSTC) enhanced with a multi-level receptive field mechanism and label smoothing regularization to jointly capture multi-scale spatial context and improve generalization. Furthermore, HyperTTA incorporates a lightweight test-time adaptation (TTA) strategy, the confidence-aware entropy-minimized LayerNorm adapter (CELA), which updates only the affine parameters of LayerNorm layers by minimizing prediction entropy on high-confidence unlabeled target samples. This confidence-aware adaptation prevents unreliable updates from noisy predictions, enabling robust and dynamic adaptation without access to source data or target annotations. Extensive experiments on two benchmark datasets demonstrate that HyperTTA outperforms existing baselines across a wide range of degradation scenarios, validating the effectiveness of both its classification backbone and the proposed TTA scheme. Code will be made available publicly.