Classifying hyperspectral remote sensing images across different scenes has recently emerged as a significant challenge. When only historical labeled images (source domain, SD) are available, it is crucial to leverage these images effectively to train a model with strong generalization ability that can be directly applied to classify unseen samples (target domain, TD). To address these challenges, this paper proposes a novel single-domain generalization (SDG) network, termed the domain-aware adversarial domain augmentation network (DADAnet) for cross-scene hyperspectral image classification (HSIC). DADAnet involves two stages: adversarial domain augmentation (ADA) and task-specific training. ADA employs a progressive adversarial generation strategy to construct an augmented domain (AD). To enhance variability in both spatial and spectral dimensions, a domain-aware spatial-spectral mask (DSSM) encoder is constructed to increase the diversity of the generated adversarial samples. Furthermore, a two-level contrastive loss (TCC) is designed and incorporated into the ADA to ensure both the diversity and effectiveness of AD samples. Finally, DADAnet performs supervised learning jointly on the SD and AD during the task-specific training stage. Experimental results on two public hyperspectral image datasets and a new Hangzhouwan (HZW) dataset demonstrate that the proposed DADAnet outperforms existing domain adaptation (DA) and domain generalization (DG) methods, achieving overall accuracies of 80.69%, 63.75%, and 87.61% on three datasets, respectively.
Huang et al. (Thu,) studied this question.