利用城市雪花网改进正射影像

IF 2.3 Q2 REMOTE SENSING
Mojdeh Ebrahimikia, Ali Hosseininaveh, Mahdi Modiri
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引用次数: 0

摘要

随着越来越多地使用无人机捕捉城市地区的图像,校正这些图像生成的正射影像上的畸变和锯齿效应已成为一个关键问题。与载人飞机相比,无人机的飞行高度较低,高空物体会产生较大的位移,因此这尤其具有挑战性。此外,基于图像的点云生成方法通常无法生成完整的点云,原因是重叠图像之间存在遮挡区域和辐射度变化,尤其是在高大物体的边界附近。为了解决这些问题,本文提出了一种新方法,利用深度学习网络(称为 urban-SnowflakeNet)改进基于图像的方法生成的点云,该方法包括以下步骤:该方法包括以下步骤:1)准备屋顶点云并对其进行归一化处理;2)使用所提出的深度学习网络完成建筑物的点云;3)将完成的建筑物点云还原为真实坐标,并将其与背景点云相结合;4)校正 DSM 并生成最终的真实正射影像图。在两个不同的图像数据集上,与最新的正射影像增强方法相比,我们的方法平均减少了 40% 的建筑物边缘失真。然而,通过在更多数据集上保持这种成功,该方法有可能提高城市地区点云的准确性和完整性,以及三维模型改进等其他应用,这需要在未来的工作中进一步测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Orthophoto improvement using urban-SnowflakeNet

Orthophoto improvement using urban-SnowflakeNet

With the increasing use of drones for capturing images in urban areas, correcting for distortion and sawtooth effects on orthophotos generated with these images has become a critical issue. This is particularly challenging due to the larger displacements generated by high objects and lower flight altitude of drones compared to crewed aircraft. In addition, image-based point cloud generation methods often fail to produce complete point clouds due to occluded areas and radiometric changes between overlapping images, especially near the borders of high objects. To address these issues, a novel method is proposed in this article for improving the generated point clouds with image-based methods using a deep learning network, called urban-SnowflakeNet, which comprises the following steps: 1) preparing and normalizing the roof's point cloud; 2) completing the point clouds of the building using the proposed deep learning network; 3) restoring the completed point clouds of the buildings to the real coordinates and combining them with the background point cloud; and, 4) correcting the DSM and generating the final true orthophotos. On two different image datasets, our method reduced distortions at the building's edges by 40% on average when compared to the most recent orthophoto enhancement method. However, by maintaining this success on more datasets, the approach has the potential to improve the accuracy and completeness of point clouds in urban regions, as well as other applications such as 3D model improvement, which require further testing in future works.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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