Target Detection via Bayesian-Morphological Saliency in High-Resolution SAR Images
Target Detection via Bayesian-Morphological Saliency in High-Resolution SAR Images
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DOI：
10.1109/tgrs.2017.2707672
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发表时间：
2017-06
影响因子：
8.2
通讯作者：
Zhaocheng Wang;L. Du;Hongtao Su
中科院分区：
文献类型：
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作者：
Zhaocheng Wang;L. Du;Hongtao Su
传统的合成孔径雷达（SAR）图像目标检测方法主要依赖于目标与杂波之间的强度差。虽然它们在高信杂比的简单场景中是有效的，但在低信杂比的复杂场景中可能会失去效果。在高分辨率SAR图像中，目标与杂波相比不仅具有高强度，而且具有特定的尺寸特征。基于这一事实，本文提出了一种基于贝叶斯-形态学显著性的高分辨率SAR图像目标检测方法，主要包括贝叶斯显著图构建和形态学显著图构建两个阶段。贝叶斯显著图通过超像素分割和贝叶斯框架，可以获得包括感兴趣目标和一些明亮杂波的明亮目标的完整结构。此外，形态显著图可以突出感兴趣的目标，同时通过目标的尺寸先验信息抑制自然和人为的杂波。在miniSAR真实的数据集上的实验结果表明，所提出的目标检测方法是有效的。
The classical target detection methods in synthetic aperture radar (SAR) images are mainly dependent on the intensity differences between the targets and clutter. Although they are effective in the simple scenes with high signal-to-clutter ratio (SCR), they may lose effectiveness in the complex scenes with low SCR. Generally, in high-resolution SAR images, the targets present not only high intensities but also specific size characteristics compared with the clutter. Based on this fact, in this paper, we propose a new target detection method for high-resolution SAR images via Bayesian-morphological saliency, which mainly contains two stages: Bayesian saliency map construction and morphological saliency map construction. The Bayesian saliency map can obtain the complete structures of the bright objects including the targets of interest and some bright clutter, via the superpixel segmentation and Bayesian framework. Furthermore, the morphological saliency map can highlight the targets of interest while suppressing both the natural and man-made clutter via the size prior information of the targets. The experimental results on the miniSAR real data set show that the proposed target detection method is effective.