Abstract
Ship detection in Synthetic Aperture Radar (SAR) images has been of great attraction over the last two decades. Being a popular method, Constant False Alarm Rate (CFAR) detection has been widely used in the SAR literature. Influenced mainly by the presence of outliers such as high-target-density situations, busy shipping lines, crowded harbors, lands and oil spills, conventional CFAR detectors suffer Probability of Detection (\({P_{D}}\)) degradation or Probability of False Alarm (\({P_{FA}}\)) increase. In this paper, we propose a new robust and fast detector named Median Absolute Deviation-CFAR (MAD-CFAR) for ship detection in SAR images embedded in heterogeneous log-normal clutter. As it is well known, the Standard Deviation around the Mean (SDM) is a spread of data measure which can be very affected by strong and/or weak outliers and non-Gaussianity of the background clutter. To alleviate this problem, we resort to the absolute deviation around the median, commonly known as the Median Absolute Deviation (MAD) measure which happens to be more resilient to outliers in multiple target situations. Simulations results show that compared to the performances of recent CFAR detectors on both simulated and real SAR images, the MAD-CFAR detector exhibits a good false alarm regulation and a high detection in a heterogeneous log-normal background.
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The simulated SAR image of Fig. 3a has been created by a MATLAB routine that we have developed. The real SAR images of Figs. 4a-b and Fig. 4c can be accessed through the links https://tpm-ds.eo.esa.int/oads/access/collection/TerraSAR-X and https://scihub.copernicus.eu, respectively.
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As a PhD student, H. Madjidi has done the research, conducted simulations on MATLAB, and mainly wrote the manuscript (first draft). As a thesis director, Prof. T. Laroussi has supervised all the milestones of this research work; from the problem formulation, to the final version of the manuscript. Finally, as a PhD student and a member of the ’Detection and Estimation’ Team, F. Farah, who also works on CFAR ship detection on SAR images, has helped to refine the MATLAB codes and discuss the simulation results.
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Madjidi, H., Laroussi, T. & Farah, F. A robust and fast CFAR ship detector based on median absolute deviation thresholding for SAR imagery in heterogeneous log-normal sea clutter. SIViP 17, 2925–2931 (2023). https://doi.org/10.1007/s11760-023-02513-2
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DOI: https://doi.org/10.1007/s11760-023-02513-2