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Publication - Professor Alin Achim

    Superpixel-Level CFAR Detectors for Ship Detection in SAR Imagery


    Pappas, O, Achim, A & Bull, D, 2018, ‘Superpixel-Level CFAR Detectors for Ship Detection in SAR Imagery’. IEEE Geoscience and Remote Sensing Letters.


    Synthetic aperture radar (SAR) is one of the most widely employed remote sensing modalities for large-scale monitoring of maritime activity. Ship detection in SAR images is a challenging task due to inherent speckle, discernible sea clutter, and the little exploitable shape information the targets present. Constant false alarm rate (CFAR) detectors, utilizing various sea clutter statistical models and thresholding schemes, are near ubiquitous in the literature. Very few of the proposed CFAR variants deviate from the classical CFAR topology; this letter proposes a modified topology, utilizing superpixels (SPs) in lieu of rectangular sliding windows to define CFAR guardbands and background. The aim is to achieve better target exclusion from the background band and reduced false detections. The performance of this modified SP-CFAR algorithm is demonstrated on TerraSAR-X and SENTINEL-1 images, achieving superior results in comparison to classical CFAR for various background distributions.

    Full details in the University publications repository