Dr. Fabien Quere Profile

Dr. Fabien Quere

at Unistellar SAS

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Proceedings Article | 28 August 2024 Poster + Paper

Enhancing space surveillance through the Unistellar collaborative observation program

P. Janin-Potiron, R. Cañameras, B. Neichel, M. Gray, F. Marchis, R. Lambert, F. Quere, G. Blaclard, A. Malvache, O. Beltramo-Martin, J. Cantegreil, P. Boutte, Y. Zancanaro, A. Gervail, C. Correia

Proceedings Volume 13094, 1309455 (2024) https://doi.org/10.1117/12.3018639

KEYWORDS: Satellites, Telescopes, Image segmentation, Satellite imaging, Simulations, Neural networks

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The DOSSA (Decentralization of Space Situational Awareness) project, led by SpaceAble in collaboration with Unistellar and the Laboratoire d’Astrophysique de Marseille, aims to enhance space surveillance through a collaborative effort involving amateur astronomers, researchers, and industrial networks, leveraging advanced technology and data collection to improve space situational awareness in an era of escalating satellite numbers. This project aims to create a comprehensive sky map of objects transiting around Earth. It benefits from a large dataset collected by the Unistellar telescope network, a global network comprised primarily of robotically controllable evScopes 1 and 2 telescopes, each featuring a 11.4 centimeter aperture diameter. We develop dedicated deep learning algorithms to account for the relatively compact diameters of the telescopes and to extend detection thresholds. Firstly, we use traditional convolutional neural networks (CNNs) based classifiers to detect images including a satellite streak, and secondly, we use UNet to identify pixels affected by such streaks. Both neural networks are trained on realistic simulations generated using our optical Fourier simulation software, by combining observed sky backgrounds and synthetic satellite streaks spanning a wide range of orbital parameters. With this strategy, we reach excellent performance on the segmentation of images in test set, with a recall of 79.6% pixels belonging to the satellites masks, at a false positive rate of 0.001%. For 90% of streak pixels recovered by the neural networks, we obtain a precision of 96.3% on the predicted satellite masks. This exceeds the performance of non-ML algorithms and paves the way to measuring accurate satellite positions over broader magnitudes ranges and down to lower S/N, in order to increase the precision on their orbital parameters.

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