Uncertainty-aware Local Bayesian Framework for Hyperspectral Image Classification with Noisy Labels

Various deep learning-based methods have greatly improved hyperspectral image (HSI) classification performance, but these models are sensitive to noisy training labels. Human annotation on remote sensing images inevitably introduced label noise, which degrades the model prediction confidence. Understanding the spatial characteristics and distribution of such annotation errors is crucial for both diagnosing dataset annotation failures and guiding effective robust learning strategies. Current noisy label learning methods pay limited attention to visualizing noise label distributions, and these approaches often exhibit poor compatibility with noise-free models. Leveraging the relationship between the prediction uncertainty and label noise, we propose a Local Bayesian Framework (LBF) for noisy HSI classification and noise labels awareness. LBF adapts standard CNN, GCN, or Transformer backbones via local Bayesian adaptation (LBA) to evaluate prediction uncertainty and employs an uncertainty-monitoring optimization strategy (U-MOS) for training. Without major architectural changes, LBF delivers accurate uncertainty maps that highlight noisy regions, suppresses overfitting to corrupted labels, and consistently improves classification robustness across four benchmark HSI datasets.