Prompt-Integrated Adversarial Unsupervised Domain Adaptation for Scene Recognition
Yangyang Yu, Shengsheng Wang, Zihao Fu · IEEE Transactions on Geoscience and Remote Sensing · 2025
With the rapid advancement of remote sensing (RS) technologies, the role of automated cross-scene recognition in environmental monitoring and resource management has become increasingly prominent. Facing the challenge of scarce annotated data in RS, unsupervised domain adaptation (UDA) technology stands out for its ability to transfer knowledge across domains. Most RS scene recognition methods based on prompt learning only optimize the textual component. They cannot flexibly and dynamically adjust textual and visual representations, which may lead to poor performance when facing complex UDA tasks. In order to solve this problem, we propose the prompt-integrated adversarial UDA for scene recognition (PADA-Net) framework, introducing a prompt-integrated method in the domain adaptation task of RS scene classification for the first time to enhance the semantic association between images and text and better align visual-linguistic representations. PADA-Net fosters cross-modal information exchange via an interactive bridging mechanism (IBM) and combines dual Meta-nets to reinforce feature discriminability. The collaborative operation of these two components constitutes a novel system for feature discrimination. Additionally, we incorporate optimal transport theory to provide meaningful gradients and geometric guidance for training, and we use game-theoretic strategies to further enhance the efficient alignment of feature distributions, thereby addressing the domain shift problem. Finally, we carry out 24 different scene recognition tasks on multiple RS benchmark datasets, such as AID and WHU-RS19, and several experimental findings verify the excellence of our suggested approach.