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    Anomaly Detection and Target Prioritization in Planetary Imagery via the Automated Global Feature Analyzer (TM) (AGFA (TM)): Progress Towards a Driver for Autonomous (CISR)-I-4 Missions

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    106391Z.pdf
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    Author
    Fink, Wolfgang
    Brooks, Alexander J. -W.
    Tarbell, Mark A.
    Affiliation
    Univ Arizona, Coll Engn, Visual & Autonomous Explorat Syst Res Lab
    Issue Date
    2018
    Keywords
    Autonomous decision making
    sensor-data-fusion framework
    objective anomaly detection
    target prioritization
    agglomerative clustering
    principal components analysis
    autonomous (CISR)-I-4 systems
    multi-tiered robotic exploration architectures
    
    Metadata
    Show full item record
    Publisher
    SPIE-INT SOC OPTICAL ENGINEERING
    Citation
    Wolfgang Fink, Alexander J.-W. Brooks, Mark A. Tarbell, "Anomaly detection and target prioritization in planetary imagery via the automated global feature analyzer (AGFA): Progress towards a driver for autonomous C4ISR missions," Proc. SPIE 10639, Micro- and Nanotechnology Sensors, Systems, and Applications X, 106391Z (14 May 2018); doi: 10.1117/12.2303795
    Journal
    MICRO- AND NANOTECHNOLOGY SENSORS, SYSTEMS, AND APPLICATIONS X
    Rights
    © 2018 SPIE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    The Automated Global Feature Analyzer (TM) (AGFA (TM)) is a generically applicable automated sensor-data-fusion, feature extraction, feature vector clustering, anomaly detection, and target prioritization framework. AGFA (TM) operates in the respective feature space delivered by the sensor(s). In this paper we provide an overview of the inner workings of AGFA (TM) and apply AGFA (TM) to planetary imagery, representative of past, current, and future planetary missions, to demonstrate its automated and objective (i.e., unbiased) anomaly detection and target prioritization (i.e., region-of interest delineation) capabilities. Imaged operational areas are locally processed via a cascade of image segmentation, visual and geometric feature extraction, agglomerative clustering, and principal components analysis. Resulting clusters are labeled based on relative size and location in feature space. Anomalous regions may be considered immediate targets for follow-up in-situ investigation by local robotic agents, which can be directed via autonomous telecommanding, e.g., as part of a Tier-Scalable Reconnaissance mission architecture. These capabilities will be essential for driving fully autonomous (CISR)-I-4 missions of the future, since the speed of light prohibits "real time" Earth-controlled conduct of planetary exploration beyond the Moon.
    ISSN
    0277-786X
    1996-756X
    DOI
    10.1117/12.2303795
    Version
    Final published version
    Sponsors
    Edward & Maria Keonjian Endowment at the University of Arizona; NASA via Arizona Space Grant Consortium (AZSGC) [NNX15AJ17H]
    Additional Links
    https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10639/2303795/Anomaly-detection-and-target-prioritization-in-planetary-imagery-via-the/10.1117/12.2303795.full?SSO=1
    ae974a485f413a2113503eed53cd6c53
    10.1117/12.2303795
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