Analysis methods for deformation detection using TLS and UAS data on the example of a landslide simulation

IF 2.6 Q2 ENGINEERING, GEOLOGICAL International Journal of Geo-Engineering Pub Date : 2024-03-14 DOI:10.1186/s40703-023-00203-z

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Abstract

Geodetic monitoring measurements (e.g., of terrain surfaces) are used to detect deformations. Terrestrial laser scanning (TLS) or unmanned aircraft systems (UAS) equipped with lightweight cameras are often utilized for land surveying, resulting in point clouds that represent the surface of the captured object. For image-based acquisition of the area of interest, point clouds must first be generated from overlapping images, for which the Structure-from-Motion (SfM) method is commonly used. To perform deformation analyses and derive changes from them, at least two temporally different measurement epochs of the same area are required. In this article, we present both point cloud- and feature-based models from TLS and SfM-based UAS point clouds. In addition, an image-based 2D approach using optical flow is applied as an example for landslide simulation to detect changes on object surfaces. To eliminate erroneous results in the analyses due to vegetation areas, the 3D data is filtered using the CANUPO algorithm. The results of this research study show, that the task of deformation detection has some challenges, depending on the use case and the methodology. The point cloud-based methods are suitable to detect pure changes between two point clouds. Also, the direction of these changes can be determined to distinguish between material uplift and downlift. In contrast, the feature-based descriptor (Fast Point Feature Histogram, FPFH) assigns pairs of points between two epochs based on similar geometry in both point clouds therewith individual movements can be detected. However, areas that have changed significantly cannot be assigned. Optical flow shows point changes in similar dimensions to the target deformations and allows deformation analysis with much less computational effort than with 3D point clouds. Considering these findings, point cloud-based method are suitable for determining surface-based information, while the feature-based and image-based methods are capable of extracting local changes.

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以滑坡模拟为例，使用 TLS 和 UAS 数据进行变形检测的分析方法

摘要大地测量监测测量（如地形表面）用于检测变形。陆地激光扫描（TLS）或配备轻型相机的无人驾驶航空器系统（UAS）通常用于土地测量，从而生成代表所拍摄物体表面的点云。在基于图像采集感兴趣区域时，必须首先从重叠图像中生成点云，为此通常使用 "运动结构"（SfM）方法。要进行形变分析并从中得出变化，至少需要对同一区域进行两次时间上不同的测量。在本文中，我们介绍了基于 TLS 和 SfM 的无人机系统点云的点云模型和基于特征的模型。此外，我们还以滑坡模拟为例，介绍了一种基于图像的二维方法，该方法利用光流来检测物体表面的变化。为了消除因植被区域而导致的错误分析结果，使用 CANUPO 算法对三维数据进行了过滤。这项研究的结果表明，形变检测任务具有一定的挑战性，这取决于使用的情况和方法。基于点云的方法适用于检测两个点云之间的纯变化。此外，还可以确定这些变化的方向，以区分材料的上浮和下沉。相比之下，基于特征的描述符（快速点特征直方图，FPFH）可根据两个点云中相似的几何形状在两个时间点之间分配点对，从而检测出单独的移动。但是，无法分配变化较大的区域。光流显示的点变化与目标变形的维度相似，与三维点云相比，光流可以用更少的计算量进行变形分析。考虑到这些发现，基于点云的方法适用于确定表面信息，而基于特征和图像的方法则能够提取局部变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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