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Space object identification and classification from hyperspectral material analysis.

Massimiliano Vasile1, Lewis Walker2, Andrew Campbell3

  • 1Mechanical and Aerospace Engineering, University of Strathclyde, James Weir Building, 75 Montrose Street, Glasgow, G1 1XJ, UK. massimiliano.vasile@strath.ac.uk.

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This summary is machine-generated.

This study introduces a pipeline for identifying space object materials using hyperspectral imaging and machine learning. It accurately classifies objects even with weathered materials or incomplete spectral libraries.

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Area of Science:

  • Space Science
  • Materials Science
  • Computer Science

Background:

  • Accurate identification of space objects is crucial for orbital debris management and space situational awareness.
  • Hyperspectral imaging offers detailed spectral signatures for material characterization.
  • Current methods may struggle with material degradation and incomplete spectral libraries.

Purpose of the Study:

  • To develop and validate a data processing pipeline for material composition analysis of unknown space objects.
  • To assess the robustness of material identification techniques under non-ideal conditions.
  • To classify space objects based on detected material compositions.

Main Methods:

  • Utilizing hyperspectral signatures from single-pixel images for material analysis.
  • Employing machine learning and least-squares spectral matching for material identification.
  • Applying supervised machine learning for object classification based on material detection.
  • Investigating the impact of material weathering and incomplete spectral libraries.

Main Results:

  • Demonstrated the capability to determine material composition from hyperspectral data.
  • Evaluated the performance of classification algorithms with weathered materials.
  • Assessed the influence of missing spectral data in the training library.
  • Presented preliminary results on space object identification and classification.

Conclusions:

  • The proposed pipeline effectively extracts material information from hyperspectral signatures.
  • The methods show resilience to challenges like material weathering and library deficiencies.
  • This approach advances the identification and classification of space objects, enhancing space situational awareness.