Surveillance of large areas of the Earth's surface is often undertaken with low resolution synthetic aperture radar (SAR) imagery from either a satellite or a plane. There is a need to process these images with automatic target detection (ATD) algorithms. Typically the targets being searched for are vehicles or small vessels, which occupy only a few resolution cells. Simple thresholding is usually inadequate for detection due to the high amount of noise in the images. Often the background has a discernible texture, and one form of detection is to search for anomalies in the texture caused by the presence of the target pixels. To perform this task a texture model must be able to model a variety of textures at run time, and also model these textures well enough to detect anomalies. We accomplish this with our multiscale nonparametric Markov random field (MRF) texture model.


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    Title :

    (Automatic) target detection in synthetic aperture radar imagery via terrain recognition


    Contributors:
    Paget, R. (author) / Homer, J. (author) / Crisp, D. (author)


    Publication date :

    2001


    Size :

    4 Seiten, 11 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English