Attention Prompt-Driven Source-Free Adaptation for Remote Sensing Images Semantic Segmentation

Recently, remote sensing images (RSIs) domain adaptation segmentation has been extensively studied. However, existing methods generally assume that source RSIs must be available, which is obviously an overly demanding condition and will increase unnecessary costs in practice. To this end, this letter takes the lead in exploring RSIs source-free adaptation segmentation, where only the offline model pretrained on the source domain and target RSIs are available. A novel method featuring prompt learning and vision foundation models is proposed, and the novelty design includes two aspects. First, to better adapt the general-purpose knowledge in the foundation model to different target RSIs, an attention-guided prompt tuning strategy is proposed, which can dynamically steer the knowledge at different layers and positions through prompts with different weights. Second, a feature alignment strategy with similarity distance is proposed for source-free domain adaptation by taking full advantage of the representation ability of the foundation model and the flexibility of prompt learning. Extensive experiments indicate that the performance of the proposed method is significantly superior to that of existing methods. Specifically, the mIoU of target RSIs has been improved by at least 3.14%similar to 4.18%.