Low-Resource Adaptation and Structural Reconstruction for Satellite Component Segmentation

Abstract

Accurate satellite component segmentation plays a fundamental role in numerous on-orbit perception tasks, including spacecraft pose estimation, autonomous robotic servicing, and space situational awareness. However, existing segmentation methods still face three major challenges: the scarcity of fine-grained annotations, the difficulty of preserving structural continuity under large pose variations, and the severe structural imbalance caused by extremely small and slender components such as antennas. To address these issues, this paper proposes LRSRNet, a low-resource spatial reconstruction network for satellite component segmentation. First, a LoRA-adapted DINOv3 foundation model is employed to efficiently transfer large-scale visual priors to the satellite domain, enabling robust structural representation under limited supervision. To exploit the complementary information encoded at different semantic levels, multi-level transformer features are uniformly aggregated for structural representation learning. Subsequently, a Converse2D-based spatial reconstruction decoder progressively restores the spatial continuity of satellite components, facilitating the recovery of fine structural details lost during hierarchical feature encoding. Furthermore, a structure-aware optimization strategy is introduced by jointly considering the geometric characteristics and category imbalance of satellite components during training, thereby improving the learning of geometrically fragile structures without sacrificing overall segmentation performance. Experimental results on a public satellite component segmentation benchmark demonstrate the effectiveness of the proposed method. LRSRNet achieves an mIoU of 77.10% on foreground categories and 82.45% over all categories. Ablation studies and visualization results further validate the contributions of the proposed representation adaptation, spatial reconstruction, and structure-aware optimization strategies.