Intelligent characterisation of space objects with hyperspectral imaging

Vasile, Massimiliano and Walker, Lewis and Dunphy, R. David and Zabalza, Jaime and Murray, Paul and Marshall, Stephen and Savitski, Vasili (2023) Intelligent characterisation of space objects with hyperspectral imaging. Acta Astronautica, 203. pp. 510-534. ISSN 0094-5765 (https://doi.org/10.1016/j.actaastro.2022.11.039)

[thumbnail of Vasile-etal-AA-2023-Intelligent-characterisation-of-space-objects-with-hyperspectral-imaging]
Preview
 Text. Filename: Vasile_etal_AA_2023_Intelligent_characterisation_of_space_objects_with_hyperspectral_imaging.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (11MB)| Preview

Abstract

This paper presents some initial results on the use of hyperspectral imaging technology and machine learning to characterise the surface composition of space objects and reconstruct their attitude motion. The paper provides a preliminary demonstration that hyperspectral and multispectral analysis of the light absorbed, emitted and reflected by space objects can be used to identify, with some degree of accuracy, the materials composing their surface. The paper introduces a high-fidelity simulation model, developed to test this concept, and a validation of the model against experimental tests in a laboratory environment. The paper shows how to unmix the spectra to provide an estimation of the materials composing the surface facing the sensor. A machine learning approach is then proposed to reconstruct the attitude motion from the time series of spectra.

CORE (COnnecting REpositories)