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Enhanced Flyby Science with Onboard Computer Vision: Tracking and Surface Feature Detection at Small Bodies.
Thomas J. Fuchs, David R. Thompson, Brian D. Bue, Julie Castillo-Rogez, Steve A. Chien, Dero Gharibian and Kiri L. Wagstaff.
Earth and Space Science, vol. 2, 10, p. 417-34, 2015
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@article{fuchs_enhanced_2015,
    title = {Enhanced {Flyby} {Science} with {Onboard} {Computer} {Vision}: {Tracking} and {Surface} {Feature} {Detection} at {Small} {Bodies}},
    volume = {2},
    issn = {2333-5084},
    shorttitle = {Enhanced flyby science with onboard computer vision},
    url = {http://onlinelibrary.wiley.com/doi/10.1002/2014EA000042/abstract},
    doi = {10.1002/2014EA000042},
    abstract = {Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.},
    language = {en},
    number = {10},
    urldate = {2015-11-22TZ},
    journal = {Earth and Space Science},
    author = {Fuchs, Thomas J. and Thompson, David R. and Bue, Brian D. and Castillo-Rogez, Julie and Chien, Steve A. and Gharibian, Dero and Wagstaff, Kiri L.},
    year = {2015},
    keywords = {0540 Image processing, 0555 Neural networks, fuzzy logic, machine learning, 6055 Surfaces, 6094 Instruments and techniques, 6205 Asteroids, asteroids, comets, computer vision, flyby, machine learning, small bodies},
    pages = {417--34}
}
Download Endnote/RIS citation
TY - JOUR
TI - Enhanced Flyby Science with Onboard Computer Vision: Tracking and Surface Feature Detection at Small Bodies
AU - Fuchs, Thomas J.
AU - Thompson, David R.
AU - Bue, Brian D.
AU - Castillo-Rogez, Julie
AU - Chien, Steve A.
AU - Gharibian, Dero
AU - Wagstaff, Kiri L.
T2 - Earth and Space Science
AB - Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.
DA - 2015///
PY - 2015
DO - 10.1002/2014EA000042
DP - Wiley Online Library
VL - 2
IS - 10
SP - 417
EP - 34
J2 - Earth and Space Science
LA - en
SN - 2333-5084
ST - Enhanced flyby science with onboard computer vision
UR - http://onlinelibrary.wiley.com/doi/10.1002/2014EA000042/abstract
Y2 - 2015/11/22/T21:25:18Z
KW - 0540 Image processing
KW - 0555 Neural networks, fuzzy logic, machine learning
KW - 6055 Surfaces
KW - 6094 Instruments and techniques
KW - 6205 Asteroids
KW - asteroids
KW - comets
KW - computer vision
KW - flyby
KW - machine learning
KW - small bodies
ER -
Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.