The potential for satellite recognition from ground-based filter photometry

J. Zachary Gazak; Matthew Phelps; Ryan Swindle; Cody Shaw; Zach Funke; Justin Fletcher

doi:10.1117/12.2666948

13 June 2023 The potential for satellite recognition from ground-based filter photometry

J. Zachary Gazak, Matthew Phelps, Ryan Swindle, Cody Shaw, Zach Funke, Justin Fletcher

Proceedings Volume 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX ; 125190A (2023) https://doi.org/10.1117/12.2666948
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Recent work demonstrates recognition of artificial satellites in spatially unresolved observations by utilizing learned spectroscopic classification (SpectraNet1 ). That proof of concept exposes critical identifying information currently lacking in catalogs used by space domain awareness stakeholders. In this work we present experiments to increase the accessibility and efficiency of SpectraNet enabled systems by probing the bandpass and resolution requirements for learned recognition of satellites. To enable affordable, off the shelf instrumentation, this work focuses on wavelength ranges accessible by Silicon-based detectors (400-1000 nanometers). While the SpectraNet proof of concept utilized a medium resolution spectrograph on a 3.6 meter telescope at 10,000 feet elevation, we show that the identifying spectral features relate to an object’s overall spectral energy density and are accessible at significantly lower spectral resolution. This finding relaxes the need for large telescopes at high altitude. We further demonstrate that the technology can be utilized via simultaneous multi-band filter photometry. Design considerations for properly obtaining simultaneous photometry are discussed. Thus this work demonstrates that−in simulation−learned spectral recognition is an effective technology from high resolution spectrographs through simultaneous multi-filter photometric instruments. We provide experiments to understand the minimum engineered system needed to perform effective learned recognition, such that the technology can be hardened and widely proliferated.

Conference Presentation

Citation Download Citation

J. Zachary Gazak, Matthew Phelps, Ryan Swindle, Cody Shaw, Zach Funke, and Justin Fletcher "The potential for satellite recognition from ground-based filter photometry", Proc. SPIE 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX , 125190A (13 June 2023); https://doi.org/10.1117/12.2666948

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