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Shadow removal based on separated illumination correction for urban aerial remote sensing images

机译:基于分离照度校正的城市航空遥感影像阴影去除

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摘要

The presence of shadows in urban aerial images degrades the image quality and reduces the application accuracy. Removing shadows and recovering the ground information is therefore a crucial issue. The existing shadow removal methods can correct the shadow information, but the inconsistency between the corrected shadow and non-shadow areas is still obvious. A novel shadow removal method based on separated illumination correction is proposed in this paper, in which the shadow removal is only performed on the shadow-related illumination. A spatially adaptive weighted total variation model is constructed to obtain the shadow-related illumination and the shadow-free reflectance. The objects in the shadows are detected based on the reflectance, and object-oriented illumination correction is then implemented to compensate the shadow regions. The shadow removal results can be obtained by combining the corrected illumination and the reflectance. Three aerial remote sensing images were selected for the experiments, and two quantitative evaluation methods are introduced: the shadow standard deviation index and classification analysis. The results are shown and compared with four existing methods by visual and quantitative assessments, which indicate that the proposed method can yield more visually natural shadow-free images and show a better performance in the quantitative indices. (C) 2019 Elsevier B.V. All rights reserved.
机译:城市空中图像中存在阴影会降低图像质量并降低应用精度。因此,去除阴影并恢复地面信息是至关重要的问题。现有的阴影去除方法可以校正阴影信息,但是校正后的阴影与非阴影区域之间的不一致仍然很明显。提出了一种基于分离照明校正的新型阴影去除方法,该方法仅对阴影相关的照明进行阴影去除。构建空间自适应加权总变化模型以获得与阴影有关的照明和无阴影反射率。根据反射率检测阴影中的对象,然后实施面向对象的照明校正以补偿阴影区域。通过组合校正后的照明和反射率可以获得阴影去除结果。选择了三幅航空遥感影像进行实验,介绍了两种定量评价方法:阴影标准差指标和分类分析。结果显示并与现有的四种方法进行视觉和定量评估比较,表明所提出的方法可以产生更多视觉上自然的无阴影图像,并且在定量指标上表现出更好的性能。 (C)2019 Elsevier B.V.保留所有权利。

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