Manifold-Aligned Consistency Contrastive Learning for Noise-Tolerant Hyperspectral Image Classification

Label noise in hyperspectral image classification (HIC) has posed a significant challenge, mainly due to the complexity of high-dimensional spectral-spatial data. Existing approaches that rely on sample predictions for verification and correction often amplify confirmation bias, leading to decision boundaries that overfit to noisy labels. To address these issues, we propose a Manifold-Aligned Consistency Contrastive Learning (MACCL) framework that establishes a mutually-guided consistency alignment mechanism between the spectral-spatial representation space and label space through manifold learning theory to combat label noise. Specifically, to mitigate the confirmation bias of noisy labels, we introduce a manifold-aligned consistency learning module. It leverages the manifold assumption in the representation space, modeling local neighborhood graphs and enforcing consistent prediction distributions via KL divergence. This aligns label-space classifications with the representation space’s local geometry, suppressing isolated noise through manifold continuity. Additionally, to combat representation degradation that causes decision boundaries to overfit noisy labels, we integrate a noise-tolerant contrastive representation learning module. By applying confidence-guided criteria, the module focuses on high-confidence sample pairs and regularizes gradients. This emphasizes clean pairs during training, boosting the model’s discriminative ability and preserving true semantic relationships. Through this mutual guidance, the contrastive learning refines the local geometric structure through discriminative representation learning, driving the representation space closer to the intrinsic data manifold, while the manifold alignment propagates geometric constraints to rectify label space corruptions. Finally, experiments on several benchmark datasets with varying levels of noise have validated the superiority of the proposed MACCL framework.