| Citation: | Yan HU, Zili SHAN, Feng GAO. Candidate Region Extraction Method for Multi-satellite and Multi-resolution SAR Ships[J]. Journal of Electronics & Information Technology, 2019, 41(4): 770-778. doi: 10.11999/JEIT180525
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The traditional methods based on CFAR and Kernel Density Estimation (KDE) for SAR ship candidate region extraction has the following defects: The choice of false alarm rate of CFAR depends on artificial experience; CFAR only models the sea clutter distribution, which poses a certain risk of missing detection to the target; When KDE is used to filter strong sea clutter, the threshold must be selected by artificial experience. These defects make the traditional method unable to adapt to complex scene, such as multi-satellite and multi-resolution. A candidate region extraction method for multi-satellite and multi-resolution SAR ships is proposed. In view of the defects of CFAR, an iterative method of mean dichotomy is proposed to approximate the target and calculate the segmentation threshold. The calculation efficiency of this method is more than 10 times higher than that of CFAR while overcoming the defects of CFAR; In view of the defects of KDE, block KDE combined with large threshold is used to filter strong sea clutter, and then seed point growth algorithm is used to reconstruct target. Because the large threshold has enough thresholds, the method can adapt to more complex scenarios. Experiments show that the proposed method has the advantages of no missed detection, self-adaptive threshold, high computational efficiency, and low false alarm rate. It has excellent multi-satellite and multi-resolution SAR ship candidate region extraction capability.
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