Artificial Satellite Trails Detection Using U-Net Deep Neural Network and Line Segment Detector Algorithm
This addresses interference in astronomical imaging for astronomers, but it is incremental as it combines existing methods.
The paper tackles the problem of detecting artificial satellite trails in astronomical images to reduce interference, achieving a detection rate over 99% for high-SNR trails and recall/precision around 75-80% on real data.
With the rapid increase in the number of artificial satellites, astronomical imaging is experiencing growing interference. When these satellites reflect sunlight, they produce streak-like artifacts in photometry images. Such satellite trails can introduce false sources and cause significant photometric errors. As a result, accurately identifying the positions of satellite trails in observational data has become essential. In this work, we propose a satellite trail detection model that combines the U-Net deep neural network for image segmentation with the Line Segment Detector (LSD) algorithm. The model is trained on 375 simulated images of satellite trails, generated using data from the Mini-SiTian Array. Experimental results show that for trails with a signal-to-noise ratio (SNR) greater than 3, the detection rate exceeds 99. Additionally, when applied to real observational data from the Mini-SiTian Array, the model achieves a recall of 79.57 and a precision of 74.56.