Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloud
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1080/01431161.2019.1683644 http://hdl.handle.net/11449/196270 |
Resumo: | This work proposes a three-step method for segmenting the roof planes of buildings in Airborne Laser Scanning (ALS) data. The first step aims at mainly avoiding the exhaustive search for planar roof faces throughout the ALS point cloud. Standard algorithms for processing ALS point cloud are used to isolate building regions. The second step of the proposed method consists in segmenting roof planes within building regions previously delimited. We use the RANdom SAmple Consensus (RANSAC) algorithm to detect roof plane points, taking into account two adaptive parameters for checking the consistency of ALS building points with the candidate planes: the distance between ALS building points and candidate planes; and the angle between the gradient vectors at ALS building points and the candidate planes' normal vector. Each ALS building point is classified as consistent if computed parameters are below corresponding thresholds, which are automatically determined by thresholding histograms constructed for both parameters. As the RANSAC algorithm can generate fragmented results, in the third step, a post-processing is accomplished to merge planes that are approximately collinear and spatially close. The results show that the proposed method works properly. However, failures occur mainly in regions affected by local anomalies such as trees and antennas. Average rates around 90% and higher than 95% have been obtained for the completeness and correction quality parameters, respectively. |
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Repositório Institucional da UNESP |
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spelling |
Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloudThis work proposes a three-step method for segmenting the roof planes of buildings in Airborne Laser Scanning (ALS) data. The first step aims at mainly avoiding the exhaustive search for planar roof faces throughout the ALS point cloud. Standard algorithms for processing ALS point cloud are used to isolate building regions. The second step of the proposed method consists in segmenting roof planes within building regions previously delimited. We use the RANdom SAmple Consensus (RANSAC) algorithm to detect roof plane points, taking into account two adaptive parameters for checking the consistency of ALS building points with the candidate planes: the distance between ALS building points and candidate planes; and the angle between the gradient vectors at ALS building points and the candidate planes' normal vector. Each ALS building point is classified as consistent if computed parameters are below corresponding thresholds, which are automatically determined by thresholding histograms constructed for both parameters. As the RANSAC algorithm can generate fragmented results, in the third step, a post-processing is accomplished to merge planes that are approximately collinear and spatially close. The results show that the proposed method works properly. However, failures occur mainly in regions affected by local anomalies such as trees and antennas. Average rates around 90% and higher than 95% have been obtained for the completeness and correction quality parameters, respectively.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Sao Paulo State Univ, Dept Cartog, 305 Roberto Simonsen St, BR-19000900 Presidente Prudente, BrazilSao Paulo State Univ, Dept Cartog, 305 Roberto Simonsen St, BR-19000900 Presidente Prudente, BrazilTaylor & Francis LtdUniversidade Estadual Paulista (Unesp)Dal Poz, Aluir P. [UNESP]Yano Ywata, Michelle S. [UNESP]2020-12-10T19:39:14Z2020-12-10T19:39:14Z2019-10-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article2047-2061http://dx.doi.org/10.1080/01431161.2019.1683644International Journal Of Remote Sensing. Abingdon: Taylor & Francis Ltd, v. 41, n. 6, p. 2047-2061, 2020.0143-1161http://hdl.handle.net/11449/19627010.1080/01431161.2019.1683644WOS:000492532700001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal Of Remote Sensinginfo:eu-repo/semantics/openAccess2024-06-18T15:01:26Zoai:repositorio.unesp.br:11449/196270Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:37:00.480780Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloud |
title |
Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloud |
spellingShingle |
Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloud Dal Poz, Aluir P. [UNESP] |
title_short |
Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloud |
title_full |
Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloud |
title_fullStr |
Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloud |
title_full_unstemmed |
Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloud |
title_sort |
Adaptive random sample consensus approach for segmentation of building roof in airborne laser scanning point cloud |
author |
Dal Poz, Aluir P. [UNESP] |
author_facet |
Dal Poz, Aluir P. [UNESP] Yano Ywata, Michelle S. [UNESP] |
author_role |
author |
author2 |
Yano Ywata, Michelle S. [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Dal Poz, Aluir P. [UNESP] Yano Ywata, Michelle S. [UNESP] |
description |
This work proposes a three-step method for segmenting the roof planes of buildings in Airborne Laser Scanning (ALS) data. The first step aims at mainly avoiding the exhaustive search for planar roof faces throughout the ALS point cloud. Standard algorithms for processing ALS point cloud are used to isolate building regions. The second step of the proposed method consists in segmenting roof planes within building regions previously delimited. We use the RANdom SAmple Consensus (RANSAC) algorithm to detect roof plane points, taking into account two adaptive parameters for checking the consistency of ALS building points with the candidate planes: the distance between ALS building points and candidate planes; and the angle between the gradient vectors at ALS building points and the candidate planes' normal vector. Each ALS building point is classified as consistent if computed parameters are below corresponding thresholds, which are automatically determined by thresholding histograms constructed for both parameters. As the RANSAC algorithm can generate fragmented results, in the third step, a post-processing is accomplished to merge planes that are approximately collinear and spatially close. The results show that the proposed method works properly. However, failures occur mainly in regions affected by local anomalies such as trees and antennas. Average rates around 90% and higher than 95% have been obtained for the completeness and correction quality parameters, respectively. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-27 2020-12-10T19:39:14Z 2020-12-10T19:39:14Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1080/01431161.2019.1683644 International Journal Of Remote Sensing. Abingdon: Taylor & Francis Ltd, v. 41, n. 6, p. 2047-2061, 2020. 0143-1161 http://hdl.handle.net/11449/196270 10.1080/01431161.2019.1683644 WOS:000492532700001 |
url |
http://dx.doi.org/10.1080/01431161.2019.1683644 http://hdl.handle.net/11449/196270 |
identifier_str_mv |
International Journal Of Remote Sensing. Abingdon: Taylor & Francis Ltd, v. 41, n. 6, p. 2047-2061, 2020. 0143-1161 10.1080/01431161.2019.1683644 WOS:000492532700001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal Of Remote Sensing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2047-2061 |
dc.publisher.none.fl_str_mv |
Taylor & Francis Ltd |
publisher.none.fl_str_mv |
Taylor & Francis Ltd |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128678623707136 |