提出闭环框架CABIN，让高光谱分类模型学会识别不确定区域并纠正错误标签。
Perceive, Act and Correct: Confidence Is Not Enough for Hyperspectral Classification
- 通过估计认知不确定性，识别易错区域，提升模型感知能力
- 在稀疏标注下，性能优于主流方法，标签利用效率显著提升
- 适合标注稀缺、类别不平衡的高光谱图像分类任务
仅依赖置信度进行高光谱图像分类容易产生误导，模型常将高预测分数误认为正确，却缺乏对不确定性的认知，导致确认偏见。尤其在标注稀疏或类别不平衡时，模型会过拟合于自信的错误，难以泛化。为此，我们提出半监督框架CABIN（Cognitive-Aware Behavior-Informed learning），通过感知、行动与纠正的闭环学习机制解决该问题。CABIN首先通过估计认知不确定性建立感知意识，识别可能出错的模糊区域；接着采用不确定性引导的双重采样策略，对不确定样本进行探索，同时将可信样本作为稳定伪标签以降低偏差；最后引入细粒度动态分配策略，将伪标签数据分为可靠、模糊和噪声三类，分别施加针对性损失以增强泛化能力。实验表明，多种前沿方法在集成CABIN后均获得性能提升，且标签使用效率显著改善。
原文摘要 · Abstract (English)
Confidence alone is often misleading in hyperspectral image classification, as models tend to mistake high predictive scores for correctness while lacking awareness of uncertainty. This leads to confirmation bias, especially under sparse annotations or class imbalance, where models overfit confident errors and fail to generalize. We propose CABIN (Cognitive-Aware Behavior-Informed learNing), a semi-supervised framework that addresses this limitation through a closed-loop learning process of perception, action, and correction. CABIN first develops perceptual awareness by estimating epistemic uncertainty, identifying ambiguous regions where errors are likely to occur. It then acts by adopting an Uncertainty-Guided Dual Sampling Strategy, selecting uncertain samples for exploration while anchoring confident ones as stable pseudo-labels to reduce bias. To correct noisy supervision, CABIN introduces a Fine-Grained Dynamic Assignment Strategy that categorizes pseudo-labeled data into reliable, ambiguous, and noisy subsets, applying tailored losses to enhance generalization. Experimental results show that a wide range of state-of-the-art methods benefit from the integration of CABIN, with improved labeling efficiency and performance.
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