Classification of Terrestrial Laser Scanner Point Clouds: A Comparison of Methods for Landslide Monitoring from Mathematical Surface Approximation

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Kermarrec, G.; Yang, Z.; Czerwonka-Schröder, D.: Classification of Terrestrial Laser Scanner Point Clouds: A Comparison of Methods for Landslide Monitoring from Mathematical Surface Approximation. In: Remote sensing 14 (2022), Nr. 20, 5099. DOI: https://doi.org/10.3390/rs14205099

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/13137

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Sum total of downloads: 233




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Abstract: 
Terrestrial laser scanners (TLS) are contact-free measuring sensors that record dense point clouds of objects or scenes by acquiring coordinates and an intensity value for each point. The point clouds are scattered and noisy. Performing a mathematical surface approximation instead of working directly on the point cloud is an efficient way to reduce the data storage and structure the point clouds by transforming “data” to “information”. Applications include rigorous statistical testing for deformation analysis within the context of landslide monitoring. In order to reach an optimal approximation, classification and segmentation algorithms can identify and remove inhomogeneous structures, such as trees or bushes, to obtain a smooth and accurate mathematical surface of the ground. In this contribution, we compare methods to perform the classification of TLS point clouds with the aim of guiding the reader through the existing algorithms. Besides the traditional point cloud filtering methods, we will analyze machine learning classification algorithms based on the manual extraction of point cloud features, and a deep learning approach with automatic extraction of features called PointNet++. We have intentionally chosen strategies easy to implement and understand so that our results are reproducible for similar point clouds. We show that each method has advantages and drawbacks, depending on user criteria, such as the computational time, the classification accuracy needed, whether manual extraction is performed or not, and if prior information is required. We highlight that filtering methods are advantageous for the application at hand and perform a mathematical surface approximation as an illustration. Accordingly, we have chosen locally refined B-splines, which were shown to provide an optimal and computationally manageable approximation of TLS point clouds.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Fakultät für Mathematik und Physik

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pos. country downloads
total perc.
1 image of flag of Germany Germany 108 46.35%
2 image of flag of United States United States 29 12.45%
3 image of flag of Russian Federation Russian Federation 8 3.43%
4 image of flag of China China 8 3.43%
5 image of flag of Austria Austria 7 3.00%
6 image of flag of India India 6 2.58%
7 image of flag of Indonesia Indonesia 5 2.15%
8 image of flag of Czech Republic Czech Republic 5 2.15%
9 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 4 1.72%
10 image of flag of Canada Canada 4 1.72%
    other countries 49 21.03%

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