Glinka Szymon, Bajer Jarosław, Wierzbicki Damian, Karwowska Kinga, Kedzierski Michal
Creotech Instruments S.A., 05-500 Piaseczno, Poland.
Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland.
Sensors (Basel). 2023 Sep 29;23(19):8162. doi: 10.3390/s23198162.
Processing single high-resolution satellite images may provide a lot of important information about the urban landscape or other applications related to the inventory of high-altitude objects. Unfortunately, the direct extraction of specific features from single satellite scenes can be difficult. However, the appropriate use of advanced processing methods based on deep learning algorithms allows us to obtain valuable information from these images. The height of buildings, for example, may be determined based on the extraction of shadows from an image and taking into account other metadata, e.g., the sun elevation angle and satellite azimuth angle. Classic methods of processing satellite imagery based on thresholding or simple segmentation are not sufficient because, in most cases, satellite scenes are not spectrally heterogenous. Therefore, the use of classical shadow detection methods is difficult. The authors of this article explore the possibility of using high-resolution optical satellite data to develop a universal algorithm for a fully automated estimation of object heights within the land cover by calculating the length of the shadow of each founded object. Finally, a set of algorithms allowing for a fully automatic detection of objects and shadows from satellite and aerial imagery and an iterative analysis of the relationships between them to calculate the heights of typical objects (such as buildings) and atypical objects (such as wind turbines) is proposed. The city of Warsaw (Poland) was used as the test area. LiDAR data were adopted as the reference measurement. As a result of final analyses based on measurements from several hundred thousand objects, the global accuracy obtained was ±4.66 m.
处理单张高分辨率卫星图像可以提供许多有关城市景观或与高空物体清查相关的其他应用的重要信息。不幸的是，从单张卫星场景中直接提取特定特征可能很困难。然而，适当地使用基于深度学习算法的先进处理方法使我们能够从这些图像中获取有价值的信息。例如，可以基于从图像中提取阴影并考虑其他元数据（例如太阳仰角和卫星方位角）来确定建筑物的高度。基于阈值处理或简单分割的传统卫星图像经典处理方法并不充分，因为在大多数情况下，卫星场景在光谱上并非异质的。因此，使用经典的阴影检测方法很困难。本文作者探讨了利用高分辨率光学卫星数据开发一种通用算法的可能性，该算法通过计算每个已识别物体的阴影长度来全自动估计土地覆盖范围内物体的高度。最后，提出了一套算法，可从卫星和航空图像中全自动检测物体和阴影，并对它们之间的关系进行迭代分析，以计算典型物体（如建筑物）和非典型物体（如风力涡轮机）的高度。以波兰华沙市作为测试区域。采用激光雷达数据作为参考测量。基于对几十万物体的测量进行最终分析的结果，获得的全局精度为±4.66米。
