Open AccessAutonomous Detection of Particles and Tracks in Optical Images
Author(s) -
Liounis Andrew J.,
Small Jeffrey L.,
Swenson Jason C.,
Lyzhoft Joshua R.,
Ashman Benjamin W.,
Getzandanner Kenneth M.,
Moreau Michael C.,
Adam Coralie D.,
Leonard Jason M.,
Nelson Derek S.,
Pelgrift John Y.,
Bos Brent J.,
Chesley Steven R.,
Hergenrother Carl W.,
Lauretta Dante S.
Publication year - 2020
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2019ea000843
Subject(s) - asteroid , osiris , tracking (education) , trojan , computer science , spacecraft , computer vision , remote sensing , regolith , artificial intelligence , particle (ecology) , astronomy , geology , physics , psychology , pedagogy , oceanography , botany , biology
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.