Exploiting Diffusion Priors for Generalizable Few-Shot Satellite Image Semantic Segmentation

Satellite segmentation is vital for spacecraft perception, supporting tasks like structural analysis, fault detection, and in-orbit servicing. However, the generalization of existing methods is severely limited by the scarcity of target satellite data and substantial morphological differences between target satellites and training samples, leading to suboptimal performance in real-world scenarios. In this work, we propose a novel diffusion-based framework for few-shot satellite segmentation, named DiffSatSeg, which leverages the powerful compositional generalization capability of diffusion models to address the challenges inherent in satellite segmentation tasks. Specifically, we propose a parameter-efficient fine-tuning strategy that fully exploits the strong prior knowledge of diffusion models while effectively accommodating the unique structural characteristics of satellites as rare targets. We further propose a segmentation mechanism based on distributional similarity, designed to overcome the limited generalization capability of conventional segmentation models when encountering novel satellite targets with substantial inter-class variations. Finally, we design a consistency learning strategy to suppress redundant texture details in diffusion features, thereby mitigating their interference in segmentation. Extensive experiments demonstrate that our method achieves state-of-the-art performance, yielding a remarkable 33.6% improvement over existing approaches even when only a single target satellite image is available. Notably, our framework also enables reference-based segmentation, which holds great potential for practical deployment and real-world applications.