SpectraNet: Learned Recognition of Artificial Satellites From High Contrast Spectroscopic Imagery

• URL: http://arxiv.org/abs/2201.03614v1
• Date: Mon, 10 Jan 2022 19:51:00 GMT
• Title: SpectraNet: Learned Recognition of Artificial Satellites From High Contrast Spectroscopic Imagery
• Authors: J. Zachary Gazak, Ian McQuaid, Ryan Swindle, Matthew Phelps, Justin Fletcher
• Abstract summary: Current methods for extracting object identification from observed data require spatially resolved imagery which limits identification to objects in low earth orbits. Most artificial satellites, however, operate in geostationary orbits at distances which prohibit ground based observatories from resolving spatial information. This paper demonstrates an object identification solution leveraging modified residual convolutional neural networks to map distance-invariant data to object identity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Effective space traffic management requires positive identification of artificial satellites. Current methods for extracting object identification from observed data require spatially resolved imagery which limits identification to objects in low earth orbits. Most artificial satellites, however, operate in geostationary orbits at distances which prohibit ground based observatories from resolving spatial information. This paper demonstrates an object identification solution leveraging modified residual convolutional neural networks to map distance-invariant spectroscopic data to object identity. We report classification accuracies exceeding 80% for a simulated 64-class satellite problem--even in the case of satellites undergoing constant, random re-orientation. An astronomical observing campaign driven by these results returned accuracies of 72% for a nine-class problem with an average of 100 examples per class, performing as expected from simulation. We demonstrate the application of variational Bayesian inference by dropout, stochastic weight averaging (SWA), and SWA-focused deep ensembling to measure classification uncertainties--critical components in space traffic management where routine decisions risk expensive space assets and carry geopolitical consequences.

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