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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    Title :

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


    Contributors:
    Sang-hong Park (author) / Joo-ho Jung (author) / Si-ho Kim (author) / Kyung-tae Kim (author)


    Publication date :

    2015-07-01


    Size :

    2114184 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English