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Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features
Compared with other point clouds, the airborne LiDAR point cloud has its own characteristics. The deep learning network PointNet++ ignores the inherent properties of airborne LiDAR point, and the classification precision is low. Therefore, we propose a framework based on the PointNet++ network. In t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9917240/ https://www.ncbi.nlm.nih.gov/pubmed/36763685 http://dx.doi.org/10.1371/journal.pone.0280346 |
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author | Nong, Xingzhong Bai, Wenfeng Liu, Guanlan |
author_facet | Nong, Xingzhong Bai, Wenfeng Liu, Guanlan |
author_sort | Nong, Xingzhong |
collection | PubMed |
description | Compared with other point clouds, the airborne LiDAR point cloud has its own characteristics. The deep learning network PointNet++ ignores the inherent properties of airborne LiDAR point, and the classification precision is low. Therefore, we propose a framework based on the PointNet++ network. In this work, we proposed an interpolation method that uses adaptive elevation weight to make full use of the objects in the airborne LiDAR point, which exhibits discrepancies in elevation distributions. The class-balanced loss function is used for the uneven density distribution of point cloud data. Moreover, the relationship between a point and its neighbours is captured, densely connecting point pairs in multiscale regions and adding centroid features to learn contextual information. Experiments are conducted on the Vaihingen 3D semantic labelling benchmark dataset and GML(B) benchmark dataset. The experiments show that the proposed method, which has additional contextual information and makes full use of the airborne LiDAR point cloud properties to support classification, achieves high accuracy and can be widely used in airborne LiDAR point classification. |
format | Online Article Text |
id | pubmed-9917240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99172402023-02-11 Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features Nong, Xingzhong Bai, Wenfeng Liu, Guanlan PLoS One Research Article Compared with other point clouds, the airborne LiDAR point cloud has its own characteristics. The deep learning network PointNet++ ignores the inherent properties of airborne LiDAR point, and the classification precision is low. Therefore, we propose a framework based on the PointNet++ network. In this work, we proposed an interpolation method that uses adaptive elevation weight to make full use of the objects in the airborne LiDAR point, which exhibits discrepancies in elevation distributions. The class-balanced loss function is used for the uneven density distribution of point cloud data. Moreover, the relationship between a point and its neighbours is captured, densely connecting point pairs in multiscale regions and adding centroid features to learn contextual information. Experiments are conducted on the Vaihingen 3D semantic labelling benchmark dataset and GML(B) benchmark dataset. The experiments show that the proposed method, which has additional contextual information and makes full use of the airborne LiDAR point cloud properties to support classification, achieves high accuracy and can be widely used in airborne LiDAR point classification. Public Library of Science 2023-02-10 /pmc/articles/PMC9917240/ /pubmed/36763685 http://dx.doi.org/10.1371/journal.pone.0280346 Text en © 2023 Nong et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Nong, Xingzhong Bai, Wenfeng Liu, Guanlan Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features |
title | Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features |
title_full | Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features |
title_fullStr | Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features |
title_full_unstemmed | Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features |
title_short | Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features |
title_sort | airborne lidar point cloud classification using pointnet++ network with full neighborhood features |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9917240/ https://www.ncbi.nlm.nih.gov/pubmed/36763685 http://dx.doi.org/10.1371/journal.pone.0280346 |
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