Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds
Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds
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DOI：
10.3390/drones3030061
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发表时间：
2019-09-01
期刊：
影响因子：
4.8
通讯作者：
Keesstra, Saskia
中科院分区：
文献类型：
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作者：
Anders, Niels;Valente, Joao;Keesstra, Saskia
数字高程模型（DEM）是地球表面的3D表示，在地貌学、水文学和生态学中有许多应用。使用无人机（UAV）获得的照片的运动恢复结构（SfM）摄影测量已经越来越多地用于获得高分辨率DEM。这些DEM是从代表整个景观的点云插值得到的，包括地形、植被和基础设施的点。到目前为止，还没有任何研究清楚地比较不同的算法过滤的植被。因此，本研究的目的是评估各种植被过滤算法的性能SFM获得的点云。比较是在地中海地区的穆尔西亚，西班牙异质植被覆盖。进行比较的过滤方法有：基于颜色的过滤，使用过度的绿色植被指数（VI），三角形不规则网络（TIN）致密化LAStools，标准方法Agisoft Photoscan（PS），迭代表面降低（ISL），以及迭代表面降低和VI方法的组合（ISL_VI）。结果表明，对于裸露地区，过滤方法之间几乎没有差异，这是可以预期的，因为几乎没有植被存在过滤。对于有灌木和树木的区域，ISL_VI和TIN方法表现最好。这些结果表明，不同的过滤技术在不同的用例中有不同程度的成功。Photoscan等商业软件中的默认过滤器可能并不总是从点云中去除不需要的植被的最佳方法，而是应使用TIN致密化算法等替代方法来获得无植被的数字地形模型（DTM）。
Digital Elevation Models (DEMs) are 3D representations of the Earth's surface and have numerous applications in geomorphology, hydrology and ecology. Structure-from-Motion (SfM) photogrammetry using photographs obtained by unmanned aerial vehicles (UAVs) have been increasingly used for obtaining high resolution DEMs. These DEMs are interpolated from point clouds representing entire landscapes, including points of terrain, vegetation and infrastructure. Up to date, there has not been any study clearly comparing different algorithms for filtering of vegetation. The objective in this study was, therefore, to assess the performance of various vegetation filter algorithms for SfM-obtained point clouds. The comparison was done for a Mediterranean area in Murcia, Spain with heterogeneous vegetation cover. The filter methods that were compared were: color-based filtering using an excessive greenness vegetation index (VI), Triangulated Irregular Networks (TIN) densification from LAStools, the standard method in Agisoft Photoscan (PS), iterative surface lowering (ISL), and a combination of iterative surface lowering and the VI method (ISL_VI). Results showed that for bare areas there was little to no difference between the filtering methods, which is to be expected because there is little to no vegetation present to filter. For areas with shrubs and trees, the ISL_VI and TIN method performed best. These results show that different filtering techniques have various degrees of success in different use cases. A default filter in commercial software such as Photoscan may not always be the best way to remove unwanted vegetation from a point cloud, but instead alternative methods such as a TIN densification algorithm should be used to obtain a vegetation-less Digital Terrain Model (DTM).