Leveraging synthetic data for star and satellite photometry

Kimmy Chang; Alex Cabello; Jeff Houchard; J. Zachary Gazak; Justin Fletcher

doi:10.1117/12.3010174

7 June 2024 Leveraging synthetic data for star and satellite photometry

Kimmy Chang, Alex Cabello, Jeff Houchard, J. Zachary Gazak, Justin Fletcher

Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 1303518 (2024) https://doi.org/10.1117/12.3010174
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

Aperture photometry is a critical method for estimating the visual magnitudes of stars and satellites, essential in Space Domain Awareness (SDA) for tasks like collision avoidance. Traditional methods have fixed aperture shapes, limiting accuracy and adaptability. We introduce a novel approach that defines pixel-specific regions for the aperture and annulus, significantly improving accuracy. Nevertheless, conventional aperture photometry is constrained by predefined equations, leading to errors and sensitivity to image conditions. To overcome these limitations, we propose a learned photometry pipeline that combines aperture photometry with machine learning. Our approach demonstrates remarkable effectiveness for both stars and satellites across diverse image conditions. We rigorously tested it on three datasets, including a custom synthetic dataset and real imagery. Our results showcase outstanding performance, with a 1.44% error in star visual magnitude estimation and a 0.64% error in satellite visual magnitude estimation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kimmy Chang, Alex Cabello, Jeff Houchard, J. Zachary Gazak, and Justin Fletcher "Leveraging synthetic data for star and satellite photometry", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 1303518 (7 June 2024); https://doi.org/10.1117/12.3010174

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