Pseudo-Label-Assisted Subdomain Adaptation for Hyperspectral Image Classification

Cross-domain classification of hyperspectral data is a critical challenge in remote sensing, especially when labels are unavailable in the target domain. Deep learning-based domain adaptation (DA) methods have been widely used in recent years. However, curren methods primarily focus on the global domain structure of the source and target domains when considering domain adaptation, neglecting the subdomain structure within each class. Additionally, current methods directly employ predicted outputs without further exploring the confidence level of the target domain samples. These limitations lead to confusion in domain adaptation and hinder effective feature selection in neural networks. In this paper, we propose the Pseudo-Label-Assisted Subdomain Adaptation (PASDA) method, which addresses these limitations by jointly considering the subdomain structure of the source and target domains and adopting a sample selection strategy. PASDA aligns the subdomains while learning domain-invariant features as a foundation. Furthermore, it selects high-quality pseudo-labeled samples from the target domain to enhance the learning of domain-invariant features. For generating pseudo-labels in the target domain, we employ the Reweighted Pruning Label Propagation (RPLPA) strategy to reweight the output of the predicted target domain. Finally, the high-confidence samples with pseudo-labels are selected to finetune the network. The entropy regularized dual classifier constraint is introduced to enhance the discriminative feature extraction ability for the target domain. Extensive experiments on three public HSI cross-domain datasets, Pavia, Houston, and HyRANK, using overall accuracy (OA), average accuracy (AA) and kappa coefficient (Kappa) as the evaluation indicators of classification performance, demonstrate the superiority of our method. Compared with the existing state-of-the-art (SOTA) unsupervised domain adaptation (UDA) methods, our method improves OA by 2% and AA by 4%.