Domain Fusion Contrastive Learning for Cross-Scene Hyperspectral Image Classification

Recently, domain adaptation (DA) methods based on contrastive learning are widely used to solve the cross-scene classification problem. However, existing contrastive learning methods only focus on source domain or target domain features, or do not adequately consider the interaction of domain information, thus the learned domain-invariant features still have large discrepancies. To address this problem, we propose a novel domain fusion contrastive learning (DFCL) framework for cross-scene hyperspectral image (HSI) classification. DFCL uses an interdomain and intradomain dual-domain fusion strategy at the feature level, which introduces domain information as a noise interference term for sample enhancement. With the interference of domain information, same category samples are pulled closer and different categories samples are pushed further apart to learn more discriminative features. In addition, we construct an intermediate domain through the source and target domains and define a feature space loss that measures domain discrepancy by feature similarity and label similarity. Finally, a progressive selection strategy based on prototype learning is proposed to select high-confidence pseudolabels for DFCL. Experiments on three HSI cross-scene datasets show that the proposed method is superior to existing DA methods.