Ship detection is one of the essential techniques for ship recognition from synthetic aperture radar (SAR) images. This paper presents a fast iterative detection procedure to eliminate the influence of target returns on the estimation of local sea clutter distributions for constant false alarm rate (CFAR) detectors. A fast block detector is first employed to extract potential target sub-images; and then, an iterative censoring CFAR algorithm is used to detect ship candidates from each target blocks adaptively and efficiently, where parallel detection is available, and statistical parameters of G0 distribution fitting local sea clutter well can be quickly estimated based on an integral image operator. Experimental results of TerraSAR-X images demonstrate the effectiveness of the proposed technique.
A procedure is proposed to reconstruct the radar cross section (RCS) of interested targets from synthetic aperture radar
(SAR) images. Key factors in imaging are considered for exact RCS reconstruction, including image defocusing from
target motion, system over-sampling, window function, zero-padding and image calibration. Experimental results for
both numerically calculated inverse SAR (ISAR) and spaceborne SAR image demonstrate the effectiveness and accuracy
of the proposed technique.
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