In the realm of Space Domain Awareness (SDA), precise photometric measurements are essential for applications such as stability analysis, shape recovery, and material studies of satellites. However, current methods that rely on manual data collection and analysis are not scalable to autonomous frameworks, which are increasingly necessary due to the growing congestion in space. This research presents an approach to automate photometric measurements within a network of telescopes operating in non-ideal conditions. Our work focuses on achieving reliable photometry in degraded weather conditions, where traditional methods might fail, leading to false detections and unnecessary follow-up efforts. We utilize the SatSim space scene simulator to generate synthetic data for training and testing photometry algorithms. These algorithms include both traditional aperture photometry and machine learning-based approaches. Our methodology employs dynamic segmentation techniques to optimize the detection of satellites and stars under various adverse conditions. The segmentation methods were evaluated for their robustness in different scenarios, with the Depth-First Search + Interquartile Range (DFS + IQR) approach showing the most promise. Through extensive experimentation, we demonstrate that our approach can achieve a photometric precision of approximately 10−1, even in adverse conditions. This represents a significant advancement in the field, as it enables more reliable satellite detection and tracking in real-world, non-photometric environments. Additionally, our ablation studies highlight the importance of balanced datasets in reducing error metrics, particularly for underrepresented satellite classes. This work contributes to the development of more effective autonomous SDA systems, capable of operating efficiently in a wide range of environmental conditions.
| Published in | American Journal of Optics and Photonics (Volume 12, Issue 2) |
| DOI | 10.11648/j.ajop.20241202.11 |
| Page(s) | 18-29 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Aperture Photometry, Machine Learning, Visual Magnitude, Synthetic Data
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APA Style
Chang, K., Cabello, A., Houchard, J., Gazak, J. Z., Fletcher, J. (2024). Leveraging Synthetic Data for Star and Satellite Photometry. American Journal of Optics and Photonics, 12(2), 18-29. https://doi.org/10.11648/j.ajop.20241202.11
ACS Style
Chang, K.; Cabello, A.; Houchard, J.; Gazak, J. Z.; Fletcher, J. Leveraging Synthetic Data for Star and Satellite Photometry. Am. J. Opt. Photonics 2024, 12(2), 18-29. doi: 10.11648/j.ajop.20241202.11
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Download@article{10.11648/j.ajop.20241202.11,
author = {Kimmy Chang and Alex Cabello and Jeff Houchard and Jonathan Zachary Gazak and Justin Fletcher},
title = {Leveraging Synthetic Data for Star and Satellite Photometry},
journal = {American Journal of Optics and Photonics},
volume = {12},
number = {2},
pages = {18-29},
doi = {10.11648/j.ajop.20241202.11},
url = {https://doi.org/10.11648/j.ajop.20241202.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajop.20241202.11},
abstract = {In the realm of Space Domain Awareness (SDA), precise photometric measurements are essential for applications such as stability analysis, shape recovery, and material studies of satellites. However, current methods that rely on manual data collection and analysis are not scalable to autonomous frameworks, which are increasingly necessary due to the growing congestion in space. This research presents an approach to automate photometric measurements within a network of telescopes operating in non-ideal conditions. Our work focuses on achieving reliable photometry in degraded weather conditions, where traditional methods might fail, leading to false detections and unnecessary follow-up efforts. We utilize the SatSim space scene simulator to generate synthetic data for training and testing photometry algorithms. These algorithms include both traditional aperture photometry and machine learning-based approaches. Our methodology employs dynamic segmentation techniques to optimize the detection of satellites and stars under various adverse conditions. The segmentation methods were evaluated for their robustness in different scenarios, with the Depth-First Search + Interquartile Range (DFS + IQR) approach showing the most promise. Through extensive experimentation, we demonstrate that our approach can achieve a photometric precision of approximately 10−1, even in adverse conditions. This represents a significant advancement in the field, as it enables more reliable satellite detection and tracking in real-world, non-photometric environments. Additionally, our ablation studies highlight the importance of balanced datasets in reducing error metrics, particularly for underrepresented satellite classes. This work contributes to the development of more effective autonomous SDA systems, capable of operating efficiently in a wide range of environmental conditions.},
year = {2024}
}
TY - JOUR T1 - Leveraging Synthetic Data for Star and Satellite Photometry AU - Kimmy Chang AU - Alex Cabello AU - Jeff Houchard AU - Jonathan Zachary Gazak AU - Justin Fletcher Y1 - 2024/09/29 PY - 2024 N1 - https://doi.org/10.11648/j.ajop.20241202.11 DO - 10.11648/j.ajop.20241202.11 T2 - American Journal of Optics and Photonics JF - American Journal of Optics and Photonics JO - American Journal of Optics and Photonics SP - 18 EP - 29 PB - Science Publishing Group SN - 2330-8494 UR - https://doi.org/10.11648/j.ajop.20241202.11 AB - In the realm of Space Domain Awareness (SDA), precise photometric measurements are essential for applications such as stability analysis, shape recovery, and material studies of satellites. However, current methods that rely on manual data collection and analysis are not scalable to autonomous frameworks, which are increasingly necessary due to the growing congestion in space. This research presents an approach to automate photometric measurements within a network of telescopes operating in non-ideal conditions. Our work focuses on achieving reliable photometry in degraded weather conditions, where traditional methods might fail, leading to false detections and unnecessary follow-up efforts. We utilize the SatSim space scene simulator to generate synthetic data for training and testing photometry algorithms. These algorithms include both traditional aperture photometry and machine learning-based approaches. Our methodology employs dynamic segmentation techniques to optimize the detection of satellites and stars under various adverse conditions. The segmentation methods were evaluated for their robustness in different scenarios, with the Depth-First Search + Interquartile Range (DFS + IQR) approach showing the most promise. Through extensive experimentation, we demonstrate that our approach can achieve a photometric precision of approximately 10−1, even in adverse conditions. This represents a significant advancement in the field, as it enables more reliable satellite detection and tracking in real-world, non-photometric environments. Additionally, our ablation studies highlight the importance of balanced datasets in reducing error metrics, particularly for underrepresented satellite classes. This work contributes to the development of more effective autonomous SDA systems, capable of operating efficiently in a wide range of environmental conditions. VL - 12 IS - 2 ER -
Space Systems Command (A&AS), Kihei, Hawaii, U.S.
