Autonomous underwater vehicle (AUV) technology has matured sufficiently to allow large areas of seafloor to be covered quickly. This has led to an increased interest in automated mine countermeasures (MCM) techniques. The majority of current models require training, and their success rate is often dependent on the similarity between the testing data and the data used to train the system. They also often produce a black box solution to the problem. Therefore, while the correct result may be obtained, it is often very difficult to determine why the model has produced such a result. The approach detailed in this article is a model-based alternative to traditional supervised models.


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    Titel :

    Mine detection and classification in side scan sonar


    Weitere Titelangaben:

    Entdeckung und Klassifikation von Minen mit einem Seitensichtsonar


    Beteiligte:
    Reed, Scott (Autor:in) / Petillot, Yvan (Autor:in) / Bell, Judith (Autor:in)

    Erschienen in:

    Sea Technology ; 45 , 11 ; 35-39


    Erscheinungsdatum :

    2004


    Format / Umfang :

    5 Seiten



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    MINE DETECTION AND CLASSIFICATION IN SIDE SCAN SONAR

    Reed, S. / Petillot, Y. / Bell, J. | British Library Online Contents | 2004




    COHERENCE-BASED TARGET DETECTION AND CLASSIFICATION FOR SIDE SCAN SONAR IMAGERY

    Tucker, J.D. / Azimi-Sadjadi, M.R. | British Library Online Contents | 2008


    CHOOSING SIDE SCAN SONAR FREQUENCIES

    McGowen, D. / Morris, R. | British Library Online Contents | 2013