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    Ship detection in SAR images: A segmentation-based approach

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    This paper deals with the problem of detecting ship targets in medium and high resolution SAR images. Achieving a controlled false alarm rate is a major problem for the presence of a highly non-homogeneous sea clutter environment due to the highly variable environmental and weather conditions in closely spaced areas. After highlighting the problems of conventional techniques, a new approach is proposed based on cascading a segmentation stage and a local CFAR detection stage. The former estimates the homogeneous back-scattering regions, while the latter detects the ship targets inside the fairly homogeneous identified regions

    Segmentation-based technique for ship detection in SAR Images

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    A novel segmentation-based ship detection scheme is proposed to cope with the typical features of sea clutter. In particular, it is shown that the standard 2D-CFAR schemes applied to both low- and high-resolution SAR images do not allow adequate control of the false-alarm rate for nonhomogeneity and non-gaussianity characteristics of back-scattering from the sea. The introduction of an appropriate first segmentation stage allows standard CFAR techniques to be applied inside homogeneous areas. Moreover, the derived approximate CFAR performance against non-gaussian clutter allows the detection threshold to be set to achieve the desired false alarm rate. The practical performance is demonstrated for both a set of low-resolution quick-look ERS-SAR images and a set of high-resolution single-look X-SAR/SIR-C images. This proved that the proposed segmentation-based scheme gives a very high ship detection capability for both sets, with a controlled number of false alarms in the presence of any structure or fluctuation of the background
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