SpaceSeg: A High-Precision Intelligent Perception Segmentation Method for Multi-Spacecraft On-Orbit Targets

Accurate segmentation of multiple on-orbit spacecraft remains difficult in deep-space imagery because the scenes contain large uniform backgrounds, fine structural details, and limited labeled data. To address this problem, we propose SpaceSeg, a segmentation framework that adapts a vision foundation model to the spacecraft domain. The framework introduces a Multi-Scale Hierarchical Attention Refinement Decoder (MSHARD) to improve cross-scale feature decoding, a Spatial Domain Adaptation Transform (SDAT) training strategy to improve robustness to representative space-imaging disturbances, and a task-oriented objective that jointly optimizes segmentation accuracy and IoU-prediction quality. A lightweight connected-component-analysis module is also integrated into the pipeline for instance-aware target organization in multi-spacecraft scenes. We further construct SpaceES, a multi-scale on-orbit multi-spacecraft semantic segmentation dataset covering four space backgrounds and 17 spacecraft types. On SpaceES, SpaceSeg achieves 89.87% mIoU and 99.98% mAcc, setting a new state of the art among all evaluated baselines, surpassing the strongest competing method by 1.38 percentage points in mIoU with 59.6% fewer parameters, and exceeding the vanilla SAM2 baseline by 5.71 percentage points. Hardware-in-the-loop simulation and real satellite-to-satellite imagery experiments further support the practical relevance of the proposed method. Dataset and code are publicly available at https://github.com/Akibaru/SpaceSeg.