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Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data
The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep L...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490216/ https://www.ncbi.nlm.nih.gov/pubmed/37687815 http://dx.doi.org/10.3390/s23177360 |
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author | Tarsha Kurdi, Fayez Amakhchan, Wijdan Gharineiat, Zahra Boulaassal, Hakim El Kharki, Omar |
author_facet | Tarsha Kurdi, Fayez Amakhchan, Wijdan Gharineiat, Zahra Boulaassal, Hakim El Kharki, Omar |
author_sort | Tarsha Kurdi, Fayez |
collection | PubMed |
description | The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep Learning (DL) classification algorithm. Two ML classification approaches are developed and compared in this context. These approaches utilize the DL Pipeline Network (DLPN), which is tailored to minimize classification errors and maximize accuracy. The geometric features calculated from a point and its neighborhood are analyzed to select the features that will be used in the input layer of the classification algorithm. To evaluate the contribution of the proposed approach, five point-clouds datasets with different urban typologies and ground topography are employed. These point clouds exhibit variations in point density, accuracy, and the type of aircraft used (drone and plane). This diversity in the tested point clouds enables the assessment of the algorithm’s efficiency. The obtained high classification accuracy between 89% and 98% confirms the efficacy of the developed algorithm. Finally, the results of the adopted algorithm are compared with both rule-based and ML algorithms, providing insights into the positioning of DL classification algorithms among other strategies suggested in the literature. |
format | Online Article Text |
id | pubmed-10490216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104902162023-09-09 Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data Tarsha Kurdi, Fayez Amakhchan, Wijdan Gharineiat, Zahra Boulaassal, Hakim El Kharki, Omar Sensors (Basel) Article The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep Learning (DL) classification algorithm. Two ML classification approaches are developed and compared in this context. These approaches utilize the DL Pipeline Network (DLPN), which is tailored to minimize classification errors and maximize accuracy. The geometric features calculated from a point and its neighborhood are analyzed to select the features that will be used in the input layer of the classification algorithm. To evaluate the contribution of the proposed approach, five point-clouds datasets with different urban typologies and ground topography are employed. These point clouds exhibit variations in point density, accuracy, and the type of aircraft used (drone and plane). This diversity in the tested point clouds enables the assessment of the algorithm’s efficiency. The obtained high classification accuracy between 89% and 98% confirms the efficacy of the developed algorithm. Finally, the results of the adopted algorithm are compared with both rule-based and ML algorithms, providing insights into the positioning of DL classification algorithms among other strategies suggested in the literature. MDPI 2023-08-23 /pmc/articles/PMC10490216/ /pubmed/37687815 http://dx.doi.org/10.3390/s23177360 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tarsha Kurdi, Fayez Amakhchan, Wijdan Gharineiat, Zahra Boulaassal, Hakim El Kharki, Omar Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data |
title | Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data |
title_full | Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data |
title_fullStr | Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data |
title_full_unstemmed | Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data |
title_short | Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data |
title_sort | contribution of geometric feature analysis for deep learning classification algorithms of urban lidar data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490216/ https://www.ncbi.nlm.nih.gov/pubmed/37687815 http://dx.doi.org/10.3390/s23177360 |
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