An Adaptive Nonlocal Regularized Shadow Removal Method for Aerial Remote Sensing Images
An Adaptive Nonlocal Regularized Shadow Removal Method for Aerial Remote Sensing Images
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DOI：
10.1109/tgrs.2012.2236562
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发表时间：
2014
影响因子：
8.2
通讯作者：
Huifang Li;Liangpei Zhang;Huanfeng Shen
中科院分区：
文献类型：
--
作者：
Huifang Li;Liangpei Zhang;Huanfeng Shen
在大多数高分辨率航空图像中，特别是在城市场景中，阴影很明显，它们的存在阻碍了图像解释和后续应用，例如分类和目标检测。目前大多数阴影去除方法都是针对自然图像提出的，而遥感图像中的阴影表现出明显的特征。因此，我们分析了航空图像中阴影的特征，在本文中，我们提出了一种使用非局部（NL）算子的新的航空图像阴影去除方法。在所提出的方法中，引入软阴影来代替传统的二值硬阴影。 NL算子用于正则化阴影尺度和更新的无阴影图像。此外，引入了空间自适应 NL 正则化来处理复合阴影。软阴影和 NL 运算符的组合可产生令人满意的无阴影结果，保留纹理并保持常规颜色。采用不同类型的阴影航空图像来验证所提出的方法，并将结果与其他两种方法进行比较。实验结果证实了该方法的有效性和软阴影方法的优势。
Shadows are evident in most aerial images with high resolutions, particularly in urban scenes, and their existence obstructs the image interpretation and the following application, such as classification and target detection. Most current shadow removal methods were proposed for natural images, whereas shadows in remote sensing images show distinct characteristics. We have therefore analyzed the characteristics of shadows in aerial images, and in this paper, we propose a new shadow removal method for aerial images, using nonlocal (NL) operators. In the proposed method, the soft shadow is introduced to replace the traditional binary hard shadow. NL operators are used to regularize the shadow scale and the updated shadow-free image. Furthermore, a spatially adaptive NL regularization is introduced to handle compound shadows. The combination of the soft shadow and NL operators yields satisfying shadow-free results, preserving textures and holding regular color. Different types of shadowed aerial images are employed to verify the proposed method, and the results are compared with two other methods. The experimental results confirm the validity of the proposed method and the advantage of the soft-shadow approach.