Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification

John McKay, Vishal Monga, Raghu G. Raj

    Research output: Contribution to journalArticle

    7 Citations (Scopus)

    Abstract

    Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture sonar. Sophisticated classification techniques can now be used in sonar automatic target recognition (ATR) to locate mines and other threatening objects. Among the most promising of these methods is sparse reconstruction-based classification (SRC), which has shown an impressive resiliency to noise, blur, and occlusion. We present a coherent strategy for expanding upon SRC for sonar ATR that retains SRC's robustness while also being able to handle targets with diverse geometric arrangements, bothersome Rayleigh noise, and unavoidable background clutter. Our method, pose-corrected sparsity (PCS), incorporates a novel interpretation of a spike and slab probability distribution toward use as a Bayesian prior for class-specific discrimination in combination with a dictionary learning scheme for localized patch extractions. Additionally, PCS offers the potential for anomaly detection in order to avoid false identifications of tested objects from outside the training set with no additional training required. Compelling results are shown using a database provided by the U.S. Naval Surface Warfare Center.

    Original languageEnglish (US)
    Article number7959649
    Pages (from-to)5563-5576
    Number of pages14
    JournalIEEE Transactions on Geoscience and Remote Sensing
    Volume55
    Issue number10
    DOIs
    StatePublished - Oct 2017

    Fingerprint

    Automatic target recognition
    Sonar
    sonar
    Synthetic aperture sonar
    Military operations
    Glossaries
    Probability distributions
    Imaging techniques
    slab
    learning
    anomaly
    method

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering
    • Earth and Planetary Sciences(all)

    Cite this

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    title = "Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification",
    abstract = "Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture sonar. Sophisticated classification techniques can now be used in sonar automatic target recognition (ATR) to locate mines and other threatening objects. Among the most promising of these methods is sparse reconstruction-based classification (SRC), which has shown an impressive resiliency to noise, blur, and occlusion. We present a coherent strategy for expanding upon SRC for sonar ATR that retains SRC's robustness while also being able to handle targets with diverse geometric arrangements, bothersome Rayleigh noise, and unavoidable background clutter. Our method, pose-corrected sparsity (PCS), incorporates a novel interpretation of a spike and slab probability distribution toward use as a Bayesian prior for class-specific discrimination in combination with a dictionary learning scheme for localized patch extractions. Additionally, PCS offers the potential for anomaly detection in order to avoid false identifications of tested objects from outside the training set with no additional training required. Compelling results are shown using a database provided by the U.S. Naval Surface Warfare Center.",
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    Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification. / McKay, John; Monga, Vishal; Raj, Raghu G.

    In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 10, 7959649, 10.2017, p. 5563-5576.

    Research output: Contribution to journalArticle

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    AU - Monga, Vishal

    AU - Raj, Raghu G.

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