STARLINC: Satellite Trail Artifact Removal using Inter-Frame Correlation

The rapid expansion of low Earth orbit satellites such asStarlink is increasingly contaminating astronomical surveys. In practice,contaminated images are often identified through inspection. However,modern surveys generate terabytes of data each night, making manualscreening infeasible and necessitating reliable automated methods forsatellite trail removal. Unfortunately, existing general-domain line de-tection methods fail to generalize to astronomical images due to do-main mismatch, which are mostly grayscale with sparse bright stars andhave a low signal-to-noise ratio. Moreover, training new models fromscratch is impractical due to the lack of large-scale annotated astro-nomical datasets. To address these challenges, we introduce STARLINC,the first ML-based framework for satellite trail removal without requir-ing tedious pixel-level annotation of astronomical images. STARLINCcombines synthetic satellite trail generation for training, inter-frame dif-ferential maps from temporally adjacent exposures to highlight transienttrails, and heatmaps to provide additional localization cues for pixel-level segmentation. Extensive experiments on real-world data demon-strate substantial improvements over baselines, establishing STARLINCas a scalable solution for next-generation astronomical surveys. Code isavailable at https://github.com/starioKim/STARLINC.