Abstract. The projection of objects on the earth’s surface caused by the sunlight produces shadows. They are inevitable in high-spatial-resolution satellite remote sensing images and reduce the accuracy of change detection, land cover classification, target recognition, and many more applications. Dark-colored land covers in these satellite images, such as water bodies, road, and soil, appear with similar spectral properties as those of shadows and often result in difficulty in shadow detection, especially in complex urban settings. We propose an object-oriented building shadow extraction method and tested it using six selected study areas from TripleSat-2 satellite imagery with 3.2-m spatial resolution. The method’s main steps include (1) selecting six image features that can highlight the shadow information and then segment the image based on edge; (2) extracting shadow region based on multiple object features; and (3) masking nonbuilding shadow regions by the shadow and dark object separation index, image features including spectral, textural, and geometric features, and contextual information. The average precision, recall, and F1-score of the shadow detection were 85.6%, 88.6%, and 87.0%, respectively, and the ranges were 73.0% to 91.0%, 76.6% to 94.1%, and 74.7% to 91.2%, respectively. Compared with multiscale segmentation, edge-based segmentation is more efficient and helpful to completely and accurately extract shadows.

中文翻译：

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TripleSat-2遥感影像中建筑物阴影的面向对象检测

摘要。由阳光引起的物体在地球表面的投影会产生阴影。它们在高空间分辨率卫星遥感图像中是不可避免的，并且会降低变化检测、土地覆盖分类、目标识别和更多应用的准确性。这些卫星图像中的深色土地覆盖物，例如水体、道路和土壤，具有与阴影相似的光谱特性，通常会导致阴影检测困难，尤其是在复杂的城市环境中。我们提出了一种面向对象的建筑物阴影提取方法，并使用来自 TripleSat-2 卫星图像的六个选定研究区域进行了测试，该方法具有 3.2 米的空间分辨率。该方法的主要步骤包括：(1)选取六个能够突出阴影信息的图像特征，然后基于边缘对图像进行分割；(2)基于多个物体特征提取阴影区域；(3) 通过阴影和暗物体分离指数、包括光谱、纹理和几何特征在内的图像特征以及上下文信息来掩盖非建筑阴影区域。阴影检测的平均精度、召回率和 F1-score 分别为 85.6%、88.6% 和 87.0%，范围分别为 73.0% 至 91.0%、76.6% 至 94.1% 和 74.7% 至 91.2%，分别。与多尺度分割相比，基于边缘的分割效率更高，有助于完整准确地提取阴影。(3) 通过阴影和暗物体分离指数、包括光谱、纹理和几何特征在内的图像特征以及上下文信息来掩盖非建筑阴影区域。阴影检测的平均精度、召回率和 F1-score 分别为 85.6%、88.6% 和 87.0%，范围分别为 73.0% 至 91.0%、76.6% 至 94.1% 和 74.7% 至 91.2%，分别。与多尺度分割相比，基于边缘的分割效率更高，有助于完整准确地提取阴影。(3) 通过阴影和暗物体分离指数、包括光谱、纹理和几何特征在内的图像特征以及上下文信息来掩盖非建筑阴影区域。阴影检测的平均精度、召回率和 F1-score 分别为 85.6%、88.6% 和 87.0%，范围分别为 73.0% 至 91.0%、76.6% 至 94.1% 和 74.7% 至 91.2%，分别。与多尺度分割相比，基于边缘的分割效率更高，有助于完整准确地提取阴影。