This paper proposes an efficient method to classify inverse synthetic aperture radar (ISAR) images. The proposed method achieves invariance to translation and rotation of ISAR images by using two-dimensional (2D) Fourier transform (FT) of ISAR images, polar mapping of the 2D FT image, and a simple nearest-neighbor classifier. In simulations using ISAR images measured in a compact range, the proposed method yielded high classification ratios with small-sized data regardless of the location of the rotation center, whereas the existing method was very sensitive to the location of it.


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    Titel :

    Efficient classification of ISAR images using 2d fourier transform and polar mapping


    Beteiligte:
    Sang-hong Park (Autor:in) / Joo-ho Jung (Autor:in) / Si-ho Kim (Autor:in) / Kyung-tae Kim (Autor:in)


    Erscheinungsdatum :

    01.07.2015


    Format / Umfang :

    2114184 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch