HyperTTA: Test-Time Adaptation for Hyperspectral Image Classification under Distribution Shifts

• URL: http://arxiv.org/abs/2509.08436v2
• Date: Mon, 22 Sep 2025 06:47:26 GMT
• Title: HyperTTA: Test-Time Adaptation for Hyperspectral Image Classification under Distribution Shifts
• Authors: Xia Yue, Anfeng Liu, Ning Chen, Chenjia Huang, Hui Liu, Zhou Huang, Leyuan Fang,
• Abstract summary: HyperTTA (Test-Time Adaptable Transformer for Hyperspectral Degradation) is a unified framework that enhances model robustness under diverse degradation conditions.<n>Test-time adaptation strategy, the Confidence-aware Entropy-minimized LayerNorm Adapter (CELA), dynamically updates only the affine parameters of LayerNorm layers.<n>Experiments on two benchmark datasets demonstrate that HyperTTA outperforms state-of-the-art baselines across a wide range of degradation scenarios.
• Score: 28.21559601586271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral image (HSI) classification models are highly sensitive to distribution shifts caused by real-world degradations such as noise, blur, compression, and atmospheric effects. To address this challenge, we propose HyperTTA (Test-Time Adaptable Transformer for Hyperspectral Degradation), a unified framework that enhances model robustness under diverse degradation conditions. First, we construct a multi-degradation hyperspectral benchmark that systematically simulates nine representative degradations, enabling comprehensive evaluation of robust classification. Based on this benchmark, we develop a Spectral--Spatial Transformer Classifier (SSTC) with a multi-level receptive field mechanism and label smoothing regularization to capture multi-scale spatial context and improve generalization. Furthermore, we introduce a lightweight test-time adaptation strategy, the Confidence-aware Entropy-minimized LayerNorm Adapter (CELA), which dynamically updates only the affine parameters of LayerNorm layers by minimizing prediction entropy on high-confidence unlabeled target samples. This strategy ensures reliable adaptation without access to source data or target labels. Experiments on two benchmark datasets demonstrate that HyperTTA outperforms state-of-the-art baselines across a wide range of degradation scenarios. Code will be made available publicly.

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