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.

中文翻译：

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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

摘要 阴影信息在合成孔径雷达解译中得到了广泛的应用，但相应的阴影检测技术在以往的研究中并未得到太多关注。在本文中，我们提出了一种基于灰度分布、显着性模型和几何匹配的层次结构，用于 SAR 图像中的阴影检测。我们发现一张高斯滤波图像的灰度分布可能在其上坡部分包含“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，然后将其用于分割候选阴影。如果没有明显失真，则提出阴影显着性模型作为替代来提取此类候选区域。通常，这些候选区域可能包含一些非阴影成分。根据阴影和物体之间的几何关系，我们设计了一种匹配策略来消除候选区域中的非阴影部分。剩下的区域是最终的阴影检测结果。在两个真实数据集上的实验，移动和静止目标捕获识别和 MiniSAR，表明我们的方法比其他两种已发表的方法表现好得多。结果证明了我们提出的算法在实际SAR阴影检测任务中的有效性和可行性。