Problem
Research questions and friction points this paper is trying to address.
satellite trail
astronomical images
domain mismatch
automated methods
large-scale annotated datasets
STARLINC: Satellite Trail Artifact Removal using Inter-Frame Correlation
🤖 AI Summary
为解决低轨卫星干扰天文观测的问题，STARLINC通过合成卫星轨迹、帧间差分图和热图等方法实现自动化的卫星轨迹移除。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
satellite trail removal
inter-frame correlation
synthetic data generation
pixel-level segmentation
astronomical images
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