发表机构
Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低轨卫星污染天文图像的问题，本文提出无需像素级标注的STARLINC框架，结合合成尾迹生成、帧间差分图和热图实现尾迹去除，性能优于基线，可扩展用于下一代天文观测。
AI 中文摘要
星链等低地球轨道卫星的快速扩张正日益污染天文观测。实际中，受污染图像常通过人工检查识别，但现代观测每晚产生数TB数据，人工筛查不可行，亟需可靠的自动化卫星尾迹去除方法。然而，现有通用域线检测方法因域不匹配无法泛化到天文图像，这类图像多为灰度图，含稀疏亮星且信噪比低；此外，因缺乏大规模标注天文数据集，从头训练新模型不可行。为应对这些挑战，本文提出STARLINC，首个无需繁琐天文图像像素级标注的基于机器学习的卫星尾迹去除框架。STARLINC结合合成卫星尾迹生成用于训练、来自时间相邻曝光的帧间差分图以突出瞬态尾迹，以及提供像素级分割额外定位线索的热图。对真实世界数据的大量实验表明，该方法较基线有显著改进，确立STARLINC为下一代天文观测的可扩展解决方案。代码可在指定URL获取。
英文摘要
The rapid expansion of low Earth orbit satellites such as Starlink is increasingly contaminating astronomical surveys. In practice, contaminated images are often identified through inspection. However, modern surveys generate terabytes of data each night, making manual screening infeasible and necessitating reliable automated methods for satellite trail removal. Unfortunately, existing general-domain line detection methods fail to generalize to astronomical images due to domain mismatch, which are mostly grayscale with sparse bright stars and have a low signal-to-noise ratio. Moreover, training new models from scratch is impractical due to the lack of large-scale annotated astronomical datasets. To address these challenges, we introduce STARLINC, the first ML-based framework for satellite trail removal without requiring tedious pixel-level annotation of astronomical images. STARLINC combines synthetic satellite trail generation for training, inter-frame differential maps from temporally adjacent exposures to highlight transient trails, and heatmaps to provide additional localization cues for pixel-level segmentation. Extensive experiments on real-world data demonstrate substantial improvements over baselines, establishing STARLINC as a scalable solution for next-generation astronomical surveys. Code is available at https://github.com/starioKim/STARLINC.
CommentsAccepted to ECCV 2026
