Unsupervised Domain Adaptation for Hyperspectral Image Classification via Masked Robustness Learning and Class Structure Regularization

Unsupervised domain adaptation has become an important approach to address spectral drift in cross-scene hyperspectral image (HSI) classification. However, existing methods suffer from two key defects in learning target domain features. First, without label supervision in the target domain, the model struggles to learn discriminative features and tends to overfit to the specific statistical properties of the source domain, resulting in insufficient generalization to the target domain. Second, while existing domain alignment methods can narrow the distribution gap between the source and target domains, they lack explicit constraints on the inter-class structure of the target domain, leading to blurred boundaries between different classes in the feature space and insufficient inter-class separability. To address these issues, this letter proposes an unsupervised domain adaptation framework based on masked robustness learning and class structure regularization (MRSR-UDA). This framework uses a masked robustness learning module to randomly mask pixels to create difficult samples with incomplete information. By using a combination of strong and weak constraints to augment view prediction consistency, MRSR-UDA forces the model to learn robust discriminative features from the context of spectral-spatial neighborhoods, thereby improving generalization to the target domain. Furthermore, a class structure regularization module is introduced to constrain the inter-class correlation matrix to remain consistent before and after data augmentation from a global class relationship perspective, explicitly optimizing inter-class separability in the target domain. The two modules are collaboratively optimized at the sample-level and the class-level to improve feature discriminability and structural rationality of the feature space, respectively. Experiments on two cross-domain HSI datasets verify the effectiveness of the proposed method.