Autonomous Detection of Particles and Tracks in Optical Images

Andrew J. Liounis, Jeffrey L. Small, Jason C. Swenson, Joshua R. Lyzhoft, Benjamin W. Ashman, Kenneth M. Getzandanner, Michael C. Moreau, Coralie D. Adam, Jason M. Leonard, Derek S. Nelson, John Y. Pelgrift, Brent J. Bos, Steven R. Chesley, Carl W. Hergenrother, Dante S. Lauretta

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


When optical navigation images acquired by the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) mission revealed the periodic ejection of particles from asteroid (101955) Bennu, it became a mission priority to quickly identify and track these objects for both spacecraft safety and scientific purposes. The large number of particles and the mission criticality rendered time-intensive manual inspection impractical. We present autonomous techniques for particle detection and tracking that were developed in response to the Bennu phenomenon but that have the capacity for general application to particles in motion about a celestial body. In an example OSIRIS-REx data set, our autonomous techniques identified 93.6% of real particle tracks and nearly doubled the number of tracks detected versus manual inspection alone.

Original languageEnglish (US)
Article numbere2019EA000843
JournalEarth and Space Science
Issue number8
StatePublished - Aug 1 2020


  • active asteroid
  • image processing
  • particle detection
  • particle tracking

ASJC Scopus subject areas

  • Environmental Science (miscellaneous)
  • Earth and Planetary Sciences(all)


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