Dual-Domain Representation Modeling With Prototype Contrastive Learning for Cross-Domain Few-Shot Scene Classification

Cross-domain few-shot scene classification (CDF-SSC) aims to establish cross-domain representation between source and target domains, endowing the model few-shot classification ability on the target domain. Recent studies have improved cross-domain representation learning by incorporating unlabeled target data into semi-supervised training with labeled source data. However, these methods struggle to effectively bridge the domain gap between source and target domains, and lack the discriminative feature description ability for unlabeled target domain data, leading to inferior cross-domain representation learning and affecting the few-shot performance. In this article, a dual-domain representation modeling with prototype contrastive learning (DMPC) structure is proposed to improve the robustness of cross-domain representation learning. In DMPC, first, a dual-domain Gaussian representation modeling is designed to model the feature statistics of both source and target data as multivariate Gaussian distributions rather than fixed values and enrich domain representation by random sampling new feature statistics. It helps bridge the domain gap at the feature level, and improves the model’s robustness and generalization to better address unpredictable variations in the target domain. Second, a pseudo-prototype contrastive learning branch is proposed to improve the discriminability of representation for limited unlabeled target data. By leveraging pseudo-prototypes derived from the classifier’s weights as dynamic anchors, it refines feature representation by clustering features of the same pseudo-class and separating those of different pseudo-classes, strengthening the model’s ability to capture distinct and consistent features within the target domain. Finally, the classifier is fine-tuned on few-shot tasks to adapt to specific categories of the target domain. Extensive experimental results exhibit impressive performance of DMPC on 12 RS cross-domain scenarios.