Artificial Satellite Trails Detection Using U-Net Deep Neural Network and Line Segment Detector Algorithm

2025-09-20

AI Summary

The proliferation of low-Earth-orbit satellites introduces low signal-to-noise ratio (SNR) streak artifacts in astronomical images, degrading photometric accuracy. To address this, we propose an end-to-end detection framework integrating U-Net with the Line Segment Detector (LSD). This is the first approach to jointly model semantic segmentation and geometric line detection: U-Net performs coarse localization of satellite trail regions, while LSD refines sub-pixel-accurate linear features, significantly enhancing robustness for weak-signal trails. Evaluated on simulated data from the Mini-SiTian Array, the method achieves >99% detection rate at SNR ≥ 3. On real observational data, it attains a recall of 79.57% and precision of 74.56%. The framework thus enables high-precision astronomical photometry and rigorous data quality control in satellite-contaminated imaging.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Detecting satellite trails in astronomical images to reduce interference
• Identifying false sources caused by satellite reflections in photometry data
• Minimizing photometric errors from artificial satellite streak artifacts

Innovation

• Uses U-Net deep neural network for image segmentation
• Combines with Line Segment Detector (LSD) algorithm
• Trained on simulated satellite trail images