Shadow detection in SAR images based on greyscale distribution, a saliency model, and geometrical matching
Shadow detection in SAR images based on greyscale distribution, a saliency model, and geometrical matching
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基于灰度分布、显着性模型和几何匹配的 SAR 图像阴影检测
DOI：
10.1080/01431161.2020.1760394
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发表时间：
2020-07
影响因子：
3.4
通讯作者：
Xuegang Wang
中科院分区：
文献类型：
--
作者：
Haixiang Li;Xuelian Yu;Yonghao Tang;Xuegang Wang
摘要阴影信息在合成孔径雷达资料解释中得到了广泛的应用，但在以往的研究中，相应的阴影检测技术却没有得到足够的重视。在本文中，我们提出了一个层次结构的基础上灰度分布，显着性模型和几何匹配的SAR图像阴影检测。我们发现高斯滤波图像的灰度分布在其上坡部分可能包含“失真”，这是阴影存在的影响。基于这种失真，可以确定全局阈值，然后用于分割候选阴影。如果没有明显的失真，阴影显着性模型提出作为替代提取这样的候选区域。通常，这些候选区域可能包含一些非阴影分量。根据阴影与目标之间的几何关系，设计了一种匹配策略，从候选区域中剔除非阴影部分。剩余区域是最终的阴影检测结果。在两个真实的数据集上的实验结果表明，该方法的性能明显优于其他两种方法。实验结果证明了该算法在实际SAR阴影检测中的有效性和可行性。
ABSTRACT Shadow information has been widely used in synthetic aperture radar interpretation, but corresponding shadow detection technology has not been given much attention in past studies. In this paper, we propose a hierarchical architecture based on greyscale distribution, a saliency model, and geometrical matching for shadow detection in SAR images. We find that the greyscale distribution of one Gaussian filtered image might contain a ‘distortion’ in its uphill part, which is the effect of shadow existence. Based on this distortion, a global threshold can be determined and then be used to segment candidate shadows. If there is no obvious distortion, a shadow saliency model is proposed as a substitute to extract such candidate areas. Usually, these candidate areas may contain some non-shadow components. According to the geometric relationships between shadow and object, we design a matching strategy to eliminate non-shadow parts from candidate regions. The remained areas are final shadow detection results. Experiments on two real datasets, Moving and Stationary Target Acquisition Recognition and MiniSAR, show that our method performs much better than two other published methods. The results demonstrate the effectiveness and feasibility of our proposed algorithm in practical SAR shadow detection tasks.
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影响因子：
2.6
作者：
TREISMAN, AM;GELADE, G
通讯作者：
GELADE, G
基于多项式拟合的高分辨率遥感影像自动阴影检测方法
影响因子：
3.4
作者：
Xue Li;Yang Shuwen;Li Yikun;Ma Jijing
通讯作者：
Ma Jijing
影响因子：
4.8
作者：
Huajian Xu;Zhiwei Yang;Guozhong Chen;G. Liao;M. Tian
通讯作者：
Huajian Xu;Zhiwei Yang;Guozhong Chen;G. Liao;M. Tian
影响因子：
8.2
作者：
Zhaocheng Wang;L. Du;Hongtao Su
通讯作者：
Zhaocheng Wang;L. Du;Hongtao Su
DOI：
10.1109/cvpr.2009.5206596
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk
通讯作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk