Discriminative sparsity for Sonar ATR

John D. Mckay, Raghu G. Raj, Vishal Monga, Jason Isaacs

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations


    Advancements in Sonar image capture have enabled researchers to apply sophisticated object identification algorithms in order to locate targets of interest in images such as mines [1] [2]. Despite progress in this field, modern sonar automatic target recognition (ATR) approaches lack robustness to the amount of noise one would expect in real-world scenarios, the capability to handle blurring incurred from the physics of image capture, and the ability to excel with relatively few training samples. We address these challenges by adapting modern sparsity-based techniques with dictionaries comprising of training from each class. We develop new discriminative (as opposed to generative) sparse representations which can help automatically classify targets in Sonar imaging. Using a simulated SAS data set from the Naval Surface Warfare Center (NSWC), we obtained compelling classification rates for multi-class problems even in cases with considerable noise and sparsity in training samples.

    Original languageEnglish (US)
    Title of host publicationOCEANS 2015 - MTS/IEEE Washington
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9780933957435
    StatePublished - Feb 8 2016
    EventMTS/IEEE Washington, OCEANS 2015 - Washington, United States
    Duration: Oct 19 2015Oct 22 2015

    Publication series

    NameOCEANS 2015 - MTS/IEEE Washington


    OtherMTS/IEEE Washington, OCEANS 2015
    Country/TerritoryUnited States

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Oceanography
    • Ocean Engineering
    • Instrumentation
    • Acoustics and Ultrasonics


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