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Comparative Analysis of Resident Space Object (RSO) Detection Methods.

Vithurshan Suthakar1, Aiden Alexander Sanvido2, Randa Qashoa1

  • 1Department of Earth and Space Science, York University, Toronto, ON M3J 1P3, Canada.

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Summary

Detecting resident space objects (RSOs) is vital for space safety. This study introduces a new annotated image dataset and evaluates frame differencing techniques for improved RSO detection and space situational awareness.

Keywords:
detection algorithmoptical imagesresident space objects (RSOs)space situational awareness (SSA)

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Area of Science:

  • Space Surveillance and Tracking
  • Astrodynamics
  • Optical Remote Sensing

Background:

  • Increasing satellite launches lead to a proliferation of resident space objects (RSOs) in Earth's orbit.
  • Accurate detection of RSOs is critical for collision avoidance and maintaining space situational awareness (SSA).
  • Limited availability of public datasets hinders the development of effective RSO detection algorithms.

Purpose of the Study:

  • To present a novel, internally curated dataset of annotated resident space object (RSO) images.
  • To evaluate the efficacy of various frame differencing techniques for RSO detection using this dataset.
  • To contribute to the advancement of algorithms for enhancing space situational awareness (SSA).

Main Methods:

  • Acquisition of low-resolution, wide-field-of-view images from a stratospheric balloon platform.
  • Annotation of images to create a dataset for RSO detection.
  • Implementation and assessment of four frame differencing algorithms: adjacent frame differencing, median frame differencing, proximity filtering and tracking, and streak detection.

Main Results:

  • The evaluated algorithms demonstrated varying degrees of success in RSO detection.
  • Precision scores ranged from 73% to 100%, with streak detection achieving the highest precision.
  • F1 scores varied from 68% to 82%, indicating the effectiveness of the tested methods in identifying RSOs.

Conclusions:

  • The developed annotated RSO image dataset supports the evaluation of detection algorithms.
  • Frame differencing techniques, particularly streak detection, show significant promise for enhancing RSO detection capabilities.
  • Further development and application of these methods can improve space situational awareness (SSA) and space traffic management.