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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...

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Autores principales: Tarsha Kurdi, Fayez, Amakhchan, Wijdan, Gharineiat, Zahra, Boulaassal, Hakim, El Kharki, Omar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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