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Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model

Accurate assessment of building damage is very important for disaster response and rescue. Traditional damage detection techniques using 2D features at a single observing angle cannot objectively and accurately reflect the structural damage conditions. With the development of unmanned aerial vehicle...

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Autores principales: Liu, Chaoxian, Sui, Haigang, Huang, Lihong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698038/
https://www.ncbi.nlm.nih.gov/pubmed/33203060
http://dx.doi.org/10.3390/s20226499
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author Liu, Chaoxian
Sui, Haigang
Huang, Lihong
author_facet Liu, Chaoxian
Sui, Haigang
Huang, Lihong
author_sort Liu, Chaoxian
collection PubMed
description Accurate assessment of building damage is very important for disaster response and rescue. Traditional damage detection techniques using 2D features at a single observing angle cannot objectively and accurately reflect the structural damage conditions. With the development of unmanned aerial vehicle photogrammetric techniques and 3D point processing, automatic and accurate damage detection for building roof and facade has become a research hotspot in recent work. In this paper, we propose a building damage detection framework based on the boundary refined supervoxel segmentation and random forest–latent Dirichlet allocation classification. First, the traditional supervoxel segmentation method is improved to segment the point clouds into good boundary refined supervoxels. Then, non-building points such as ground and vegetation are removed from the generated supervoxels. Next, latent Dirichlet allocation (LDA) model is used to construct the high-level feature representation for each building supervoxel based on the selected 2D image and 3D point features. Finally, LDA model and random forest algorithm are employed to identify the damaged building regions. This method is applied to oblique photogrammetric point clouds collected from the Beichuan Country Earthquake Site. The research achieves the 3D damage assessment for building facade and roof. The result demonstrates that the proposed framework is capable of achieving around 94% accuracy for building point extraction and around 90% accuracy for damage identification. Moreover, both of the precision and recall for building damage detection reached around 89%. Concluded from comparison analysis, the proposed method improved the damage detection accuracy and the highest improvement ratio is over 8%.
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spelling pubmed-76980382020-11-29 Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model Liu, Chaoxian Sui, Haigang Huang, Lihong Sensors (Basel) Article Accurate assessment of building damage is very important for disaster response and rescue. Traditional damage detection techniques using 2D features at a single observing angle cannot objectively and accurately reflect the structural damage conditions. With the development of unmanned aerial vehicle photogrammetric techniques and 3D point processing, automatic and accurate damage detection for building roof and facade has become a research hotspot in recent work. In this paper, we propose a building damage detection framework based on the boundary refined supervoxel segmentation and random forest–latent Dirichlet allocation classification. First, the traditional supervoxel segmentation method is improved to segment the point clouds into good boundary refined supervoxels. Then, non-building points such as ground and vegetation are removed from the generated supervoxels. Next, latent Dirichlet allocation (LDA) model is used to construct the high-level feature representation for each building supervoxel based on the selected 2D image and 3D point features. Finally, LDA model and random forest algorithm are employed to identify the damaged building regions. This method is applied to oblique photogrammetric point clouds collected from the Beichuan Country Earthquake Site. The research achieves the 3D damage assessment for building facade and roof. The result demonstrates that the proposed framework is capable of achieving around 94% accuracy for building point extraction and around 90% accuracy for damage identification. Moreover, both of the precision and recall for building damage detection reached around 89%. Concluded from comparison analysis, the proposed method improved the damage detection accuracy and the highest improvement ratio is over 8%. MDPI 2020-11-13 /pmc/articles/PMC7698038/ /pubmed/33203060 http://dx.doi.org/10.3390/s20226499 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Chaoxian
Sui, Haigang
Huang, Lihong
Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model
title Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model
title_full Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model
title_fullStr Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model
title_full_unstemmed Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model
title_short Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model
title_sort identification of building damage from uav-based photogrammetric point clouds using supervoxel segmentation and latent dirichlet allocation model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698038/
https://www.ncbi.nlm.nih.gov/pubmed/33203060
http://dx.doi.org/10.3390/s20226499
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