Automatic CFAR Ship Detection in Synthetic Aperture Radar Imagery - Madjidi, Hicham;Laroussi, Toufik
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In this research, we address the problem of automatic ship detection acquires from SAR (Synthetic Aperture Radar) images in complex marine environments. Assuming a non-Gaussian sea clutter with no prior knowledge about the presence or not of any clutter edge and/or interfering targets in the sliding reference window, we propose and analyze the detection performances of three CFAR (Constant False Alarm Rate) detectors in homogeneous and heterogeneous Log-normal or Weibull sea clutter.

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Produktbeschreibung
In this research, we address the problem of automatic ship detection acquires from SAR (Synthetic Aperture Radar) images in complex marine environments. Assuming a non-Gaussian sea clutter with no prior knowledge about the presence or not of any clutter edge and/or interfering targets in the sliding reference window, we propose and analyze the detection performances of three CFAR (Constant False Alarm Rate) detectors in homogeneous and heterogeneous Log-normal or Weibull sea clutter.
Autorenporträt
Hicham Madjidi was born in Djelfa, Algeria. He received a B.S. degree in Computer Science in 2013, an M.Sc. degree in Networking and Telecommunications in 2018, both from the University of Djelfa, and a Doctorate degree in Signals and Telecommunication Systems from the University of Constantine 1 in 2023. His current research interests include SAR.