Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://ojs.c3sl.ufpr.br/ojs2/index.php/bcg/article/view/11817 http://hdl.handle.net/11449/6697 |
Resumo: | This paper proposes a methodology for automatic extraction of building roof contours from a Digital Elevation Model (DEM), which is generated through the regularization of an available laser point cloud. The methodology is based on two steps. First, in order to detect high objects (buildings, trees etc.), the DEM is segmented through a recursive splitting technique and a Bayesian merging technique. The recursive splitting technique uses the quadtree structure for subdividing the DEM into homogeneous regions. In order to minimize the fragmentation, which is commonly observed in the results of the recursive splitting segmentation, a region merging technique based on the Bayesian framework is applied to the previously segmented data. The high object polygons are extracted by using vectorization and polygonization techniques. Second, the building roof contours are identified among all high objects extracted previously. Taking into account some roof properties and some feature measurements (e. g., area, rectangularity, and angles between principal axes of the roofs), an energy function was developed based on the Markov Random Field (MRF) model. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM's showed that the methodology works properly, as it delivered roof contours with approximately 90% shape accuracy and no false positive was verified. |
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Repositório Institucional da UNESP |
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Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de MarkovAutomatic extraction of building roof contours by laser scanning data and markov random fieldAutomatic ExtractionBuilding Roof ContoursDigital Elevation ModelLaser Scanning DataMarkov Random FieldThis paper proposes a methodology for automatic extraction of building roof contours from a Digital Elevation Model (DEM), which is generated through the regularization of an available laser point cloud. The methodology is based on two steps. First, in order to detect high objects (buildings, trees etc.), the DEM is segmented through a recursive splitting technique and a Bayesian merging technique. The recursive splitting technique uses the quadtree structure for subdividing the DEM into homogeneous regions. In order to minimize the fragmentation, which is commonly observed in the results of the recursive splitting segmentation, a region merging technique based on the Bayesian framework is applied to the previously segmented data. The high object polygons are extracted by using vectorization and polygonization techniques. Second, the building roof contours are identified among all high objects extracted previously. Taking into account some roof properties and some feature measurements (e. g., area, rectangularity, and angles between principal axes of the roofs), an energy function was developed based on the Markov Random Field (MRF) model. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM's showed that the methodology works properly, as it delivered roof contours with approximately 90% shape accuracy and no false positive was verified.Univ Estado Mato Grosso, Dept Matemat, BR-78390000 Barra do Bugres, MT, BrazilUniv Estadual Paulista, Fac Ciencias & Tecnol, BR-19060900 Presidente Prudente, SP, BrazilUniv Estadual Paulista, Programa Posgrad Ciencias Cartograf, BR-19060900 Presidente Prudente, SP, BrazilUniv Estadual Paulista, Fac Ciencias & Tecnol, BR-19060900 Presidente Prudente, SP, BrazilUniv Estadual Paulista, Programa Posgrad Ciencias Cartograf, BR-19060900 Presidente Prudente, SP, BrazilUniversidade Federal do Paraná (UFPR), Centro PolitecnicoUniv Estado Mato GrossoUniversidade Estadual Paulista (Unesp)Dos Santos Galvanin, Edineia Aparecida [UNESP]Dal Poz, Aluir Porfírio [UNESP]Pires de Souza, Aparecida Doniseti [UNESP]2014-05-20T13:22:42Z2014-05-20T13:22:42Z2008-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article221-241application/pdfhttp://ojs.c3sl.ufpr.br/ojs2/index.php/bcg/article/view/11817Boletim de Ciências Geodesicas. Curitiba Pr: Universidade Federal do Paraná (UFPR), Centro Politecnico, v. 14, n. 2, p. 221-241, 2008.1413-4853http://hdl.handle.net/11449/6697WOS:000260626000005WOS000260626000005.pdf2628413289391037Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporBoletim de Ciências Geodésicas2406770,188info:eu-repo/semantics/openAccess2024-06-18T15:02:06Zoai:repositorio.unesp.br:11449/6697Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:09:54.409271Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov Automatic extraction of building roof contours by laser scanning data and markov random field |
title |
Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov |
spellingShingle |
Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov Dos Santos Galvanin, Edineia Aparecida [UNESP] Automatic Extraction Building Roof Contours Digital Elevation Model Laser Scanning Data Markov Random Field |
title_short |
Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov |
title_full |
Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov |
title_fullStr |
Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov |
title_full_unstemmed |
Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov |
title_sort |
Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov |
author |
Dos Santos Galvanin, Edineia Aparecida [UNESP] |
author_facet |
Dos Santos Galvanin, Edineia Aparecida [UNESP] Dal Poz, Aluir Porfírio [UNESP] Pires de Souza, Aparecida Doniseti [UNESP] |
author_role |
author |
author2 |
Dal Poz, Aluir Porfírio [UNESP] Pires de Souza, Aparecida Doniseti [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Univ Estado Mato Grosso Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Dos Santos Galvanin, Edineia Aparecida [UNESP] Dal Poz, Aluir Porfírio [UNESP] Pires de Souza, Aparecida Doniseti [UNESP] |
dc.subject.por.fl_str_mv |
Automatic Extraction Building Roof Contours Digital Elevation Model Laser Scanning Data Markov Random Field |
topic |
Automatic Extraction Building Roof Contours Digital Elevation Model Laser Scanning Data Markov Random Field |
description |
This paper proposes a methodology for automatic extraction of building roof contours from a Digital Elevation Model (DEM), which is generated through the regularization of an available laser point cloud. The methodology is based on two steps. First, in order to detect high objects (buildings, trees etc.), the DEM is segmented through a recursive splitting technique and a Bayesian merging technique. The recursive splitting technique uses the quadtree structure for subdividing the DEM into homogeneous regions. In order to minimize the fragmentation, which is commonly observed in the results of the recursive splitting segmentation, a region merging technique based on the Bayesian framework is applied to the previously segmented data. The high object polygons are extracted by using vectorization and polygonization techniques. Second, the building roof contours are identified among all high objects extracted previously. Taking into account some roof properties and some feature measurements (e. g., area, rectangularity, and angles between principal axes of the roofs), an energy function was developed based on the Markov Random Field (MRF) model. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM's showed that the methodology works properly, as it delivered roof contours with approximately 90% shape accuracy and no false positive was verified. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01 2014-05-20T13:22:42Z 2014-05-20T13:22:42Z |
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://ojs.c3sl.ufpr.br/ojs2/index.php/bcg/article/view/11817 Boletim de Ciências Geodesicas. Curitiba Pr: Universidade Federal do Paraná (UFPR), Centro Politecnico, v. 14, n. 2, p. 221-241, 2008. 1413-4853 http://hdl.handle.net/11449/6697 WOS:000260626000005 WOS000260626000005.pdf 2628413289391037 |
url |
http://ojs.c3sl.ufpr.br/ojs2/index.php/bcg/article/view/11817 http://hdl.handle.net/11449/6697 |
identifier_str_mv |
Boletim de Ciências Geodesicas. Curitiba Pr: Universidade Federal do Paraná (UFPR), Centro Politecnico, v. 14, n. 2, p. 221-241, 2008. 1413-4853 WOS:000260626000005 WOS000260626000005.pdf 2628413289391037 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Boletim de Ciências Geodésicas 240677 0,188 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
221-241 application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal do Paraná (UFPR), Centro Politecnico |
publisher.none.fl_str_mv |
Universidade Federal do Paraná (UFPR), Centro Politecnico |
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_ |
1808128239807234048 |