Classification of Aerial Photogrammetric 3D Point Clouds

Becker, Carlos; Rosinskaya, Elena; Häni, Nicolai; d'Angelo, Emmanuel; Strecha, Christoph

doi:10.14358/pers.84.5.287

Cited by 57 publications

(29 citation statements)

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“…The raster DEM was generated by selecting the “Point cloud classification” parameter (Table 2). Terrain extraction and DEM generation is based on fully automatic custom machine learning algorithms that classify the dense point cloud generated in typical semantic class labels, e.g., bare earth, buildings, vegetation and roads [64]. The user has no prior control in the training of the classification algorithms.…”

Section: Equipment and Methodsmentioning

confidence: 99%

DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations

Villanueva

Martínez

Montiel

2019

Sensors

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Geospatial products, such as digital elevation models (DEMs), are important topographic tools for tackling local flood studies. This study investigates the contribution of LiDAR elevation data in DEM generation based on fixed-wing unmanned aerial vehicle (UAV) imaging for flood applications. More specifically, it assesses the accuracy of UAV-derived DEMs using the proposed LiDAR-derived control point (LCP) method in a Structure-from-Motion photogrammetry processing. Also, the flood estimates (volume and area) of the UAV terrain products are compared with a LiDAR-based reference. The applied LCP-georeferencing method achieves an accuracy comparable with other studies. In addition, it has the advantage of using semi-automatic terrain data classification and is readily applicable in flood studies. Lastly, it proves the complementarity between LiDAR and UAV photogrammetry at the local level.

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Section: Equipment and Methodsmentioning

confidence: 99%

DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations

Villanueva

Martínez

Montiel

2019

Sensors

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“…[24][25][26] Some of the methods used, depending on the field, are presented in the following works: classification of point cloud data by using local neighborhood statistics, 27,28 usage of geometric features for classification, 29 employment of a covariance descriptor for classification, 30 and others. [31][32][33] Currently, deep learning in the imaging domain mostly focuses on treating 2-D images for various purposes. Usages of other types of data, including point clouds, for the purpose of deep learning are being researched, but they are still underrepresented.…”

Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

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Usage of a three-dimensional (3-D) sensor and point clouds provides various benefits over the usage of a traditional camera for industrial inspection. We focus on the development of a classification solution for industrial inspection purposes using point clouds as an input. The developed approach employs deep learning to classify point clouds, acquired via a 3-D sensor, the final goal being to verify the presence of certain industrial elements in the scene. We possess the computer-aided design model of the whole mechanical assembly and an in-house developed localization module provides initial pose estimation from which 3-D point clouds of the elements are inferred. The accuracy of this approach is proved to be acceptable for industrial usage. Robustness of the classification module in relation to the accuracy of the localization algorithm is also estimated. Fig. 1 (a) Robot-based inspection system with three cameras mounted on an end effector and (b) Ensenso N35 3-D sensor.

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“…To reduce the computational effort for querying neighbours in our highresolution point clouds, with increasing search radius these points are selected from a gradually more subsampled point cloud as proposed by Hackel et al (2016). The most common features for classification of 3D point clouds are based on the structural tensor (Becker et al, 2018). If Eigenvalues and -vectors are extracted from this covariance matrix, characteristics of the point distribution within a local neighborhood can be obtained.…”

Section: Semantic Segmentation Of Point Cloudsmentioning

confidence: 99%

“…However, these RGB values were transformed to the HSV colour space for classification. To enable a better generalization, these values are additionally averaged within each neighbourhood (Becker et al, 2018). To further benefit from the high resolution imagery, we generate an orthophoto and perform a semantic segmentation using SegNet (Badrinarayanan et al 2017).…”

Section: Semantic Segmentation Of Point Cloudsmentioning

confidence: 99%

Hybrid Georeferencing, Enhancement and Classification of Ultra-High Resolution Uav Lidar and Image Point Clouds for Monitoring Applications

Haala

Kölle

Cramer

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture by combining both imagery and LiDAR data. Our work is motivated by a project aiming at the monitoring of subsidence in an area of mixed use. Thus, it covers built-up regions in a village with a ship lock as the main object of interest as well as regions of agricultural use. In order to monitor potential subsidence in the order of 10 mm/year, we aim at sub-centimeter accuracies of the respective 3D point clouds. We show that hybrid georeferencing helps to increase the accuracy of the adjusted LiDAR point cloud by integrating results from photogrammetric block adjustment to improve the time-dependent trajectory corrections. As our main contribution, we demonstrate that joint orientation of laser scans and images in a hybrid adjustment framework significantly improves the relative and absolute height accuracies. By these means, accuracies corresponding to the GSD of the integrated imagery can be achieved. Image data can also help to enhance the LiDAR point clouds. As an example, integrating results from Multi-View Stereo potentially increases the point density from airborne LiDAR. Furthermore, image texture can support 3D point cloud classification. This semantic segmentation discussed in the final part of the paper is a prerequisite for further enhancement and analysis of the captured point cloud.

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Classification of Aerial Photogrammetric 3D Point Clouds

Cited by 57 publications

References 0 publications

DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations

DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations

Classification using a three-dimensional sensor in a structured industrial environment

Hybrid Georeferencing, Enhancement and Classification of Ultra-High Resolution Uav Lidar and Image Point Clouds for Monitoring Applications

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