用U-Net与LSD算法精准识别天文图像中的卫星轨迹
Artificial Satellite Trails Detection Using U-Net Deep Neural Network and Line Segment Detector Algorithm
- 结合U-Net分割与LSD算法检测卫星轨迹
- SNR>3时检测率超99%，真实数据召回率达79.57%
- 适合处理高密度卫星干扰的天文观测数据
随着人造卫星数量快速增长，天文成像受到日益严重的干扰。当卫星反射阳光时，会在测光图像中产生条纹状伪影，导致误检源并引发显著测光误差。因此，准确识别观测数据中的卫星轨迹至关重要。本文提出一种融合U-Net深度神经网络与直线段检测（LSD）算法的卫星轨迹检测模型。模型在375张基于Mini-SiTian Array数据生成的模拟图像上训练。实验表明，当信号噪声比（SNR）大于3时，检测率超过99%；应用于Mini-SiTian Array真实观测数据时，召回率为79.57%，精确率为74.56%。
原文摘要 · Abstract (English)
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.
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