UPetu: A Unified Parameter-Efficient Fine-Tuning Framework for Remote Sensing Foundation Model

Recent advancements in remote sensing foundation models have unveiled their tremendous potential in addressing Earth observation tasks. Presently, when large-scale foundation models are transferred to downstream tasks, the prevalent approach is to adopt the full-tuning strategy, resulting in significant increases in storage demands and computational costs. Although the introduction of parameter-efficient fine-tuning (PEFT) has mitigated this issue to some extent, mainstream PEFT methods are primarily designed for classification tasks and often prove insufficient to meet the demands of dense prediction tasks. To overcome the aforementioned limitations, we propose a unified PEFT framework UPetu, encompassing two essential and complementary modules: the efficient quantization adapter module (EQAM) and the context-aware prompt module (CAPM). EQAM is specifically designed to enhance the correlation between fine-grained feature information and task-specific knowledge through the introduction of quantization linear (Q-Linear) layers and nonlinear activation functions. In addition, CAPM is introduced to acquire rich contextual features by incorporating trainable prompts into multiscale features. The synergistic integration of both the modules enhances the representation learning capability and generalization transferability of the foundation model. Extensive experiments on three remote sensing scene classification datasets demonstrate the superiority of UPetu over other fine-tuning methods. With the update of only 0.73% of ConvNeXt-B parameters, our UPetu achieves superior performance compared with full-tuning on the UCM-55, AID-28, and AID-55 datasets. Furthermore, experiments conducted on semantic segmentation and change detection tasks provide additional evidence of the effectiveness and generalization capabilities of the proposed UPetu.