Semantic-Aware Contrastive Adaptation Bridges Domain Discrepancy for Unsupervised Remote Sensing

Abstract

Remote sensing image classification is pivotal in applications ranging from environmental monitoring to urban planning. However, the scarcity of labeled data in target domains often impedes the generalization of models trained on source domains. To address this challenge, we propose Semantic-Aware Contrastive Adaptation (SACA), a novel framework that leverages contrastive learning to construct a discriminative and class-balanced embedding space. SACA emphasizes semantic details by contrasting samples with category centroids and other samples in a distribution-aware manner. It first identifies distinctive features using category centroids as semantic anchors, then expands the sample scope by incorporating statistical information from the labeled source domain. This approach enables the embedding space to better reflect the true distribution of each semantic class. Our rigorous theoretical analysis reveals that SACA implicitly considers an infinite number of similar and dissimilar sample pairs, leading to a tighter upper bound on the contrastive loss compared to existing methods. This not only improves computational efficiency but also enhances the robustness of learned representations. To demonstrate SACA’s effectiveness, we conduct extensive experiments on three challenging remote sensing datasets: Hyperion, National Center for Airborne Laser Mapping, and Worldview-2. The results show that SACA significantly boosts the performance of self-training methods, outperforming state-of-the-art approaches by a large margin. In-depth ablation studies provide insights into the contribution of each SACA component. SACA represents a significant step towards bridging the domain gap in remote sensing image classification. By leveraging semantic-aware contrastive learning, it enables models to capture consistent semantic concepts across domains, enhancing transferability and robustness. This work opens new avenues for research in domain adaptation and has the potential to benefit a wide range of real-world applications, from precision agriculture to disaster response.

Keywords

• Domain adaptation
• remote sensing
• contrastive learning