Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1080/19479832.2018.1469547 http://hdl.handle.net/11449/177127 |
Resumo: | The problem of automated urban road network extraction is extremely complex because roads in urban scenes strongly interact with other objects. This problem can be simplified if road regions are first isolated using a classification procedure. The isolated road regions can be posteriorly used in tasks of refinement and reconstruction of the road network. This article addresses only the problem of road regions’ detection using Artificial Neural Network as classification method. However, in urban areas, the use of spectral data alone commonly leads to the confusion of the road class with other classes in RGB images, such as building roofs and concrete, because these objects may present similar spectral characteristics. To overcome this problem, it is proposed the integration of a high-resolution RGB aerial image with laser-derived images. The classification results showed that the integration of the geometric (height) and radiometric (laser pulse intensity) laser data significantly improved the classification accuracy, also contributing for the better detection of road pixel. The laser intensity data help to overcome the effects of road obstructions caused by shadows and trees. On the other hand, the laser height data help to separate the aboveground objects from those on the ground level. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification methodairborne laser dataArtificial neural networkRGB aerial imageThe problem of automated urban road network extraction is extremely complex because roads in urban scenes strongly interact with other objects. This problem can be simplified if road regions are first isolated using a classification procedure. The isolated road regions can be posteriorly used in tasks of refinement and reconstruction of the road network. This article addresses only the problem of road regions’ detection using Artificial Neural Network as classification method. However, in urban areas, the use of spectral data alone commonly leads to the confusion of the road class with other classes in RGB images, such as building roofs and concrete, because these objects may present similar spectral characteristics. To overcome this problem, it is proposed the integration of a high-resolution RGB aerial image with laser-derived images. The classification results showed that the integration of the geometric (height) and radiometric (laser pulse intensity) laser data significantly improved the classification accuracy, also contributing for the better detection of road pixel. The laser intensity data help to overcome the effects of road obstructions caused by shadows and trees. On the other hand, the laser height data help to separate the aboveground objects from those on the ground level.Department of Environmental Engineering, São Paulo State University (Unesp), São José dos Campos, BrazilDepartment of Cartography, São Paulo State University (Unesp), Presidente Prudente, BrazilUniversidade Estadual Paulista (Unesp)Mendes, Tatiana Sussel GonçalvesDal Poz, Aluir Porfírio2018-12-11T17:24:06Z2018-12-11T17:24:06Z2018-05-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1-21application/pdfhttp://dx.doi.org/10.1080/19479832.2018.1469547International Journal of Image and Data Fusion, p. 1-21.1947-98241947-9832http://hdl.handle.net/11449/17712710.1080/19479832.2018.14695472-s2.0-850464671782-s2.0-85046467178.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Image and Data Fusion0,6970,697info:eu-repo/semantics/openAccess2024-06-18T15:01:52Zoai:repositorio.unesp.br:11449/177127Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-18T15:01:52Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method |
title |
Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method |
spellingShingle |
Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method Mendes, Tatiana Sussel Gonçalves airborne laser data Artificial neural network RGB aerial image |
title_short |
Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method |
title_full |
Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method |
title_fullStr |
Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method |
title_full_unstemmed |
Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method |
title_sort |
Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method |
author |
Mendes, Tatiana Sussel Gonçalves |
author_facet |
Mendes, Tatiana Sussel Gonçalves Dal Poz, Aluir Porfírio |
author_role |
author |
author2 |
Dal Poz, Aluir Porfírio |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Mendes, Tatiana Sussel Gonçalves Dal Poz, Aluir Porfírio |
dc.subject.por.fl_str_mv |
airborne laser data Artificial neural network RGB aerial image |
topic |
airborne laser data Artificial neural network RGB aerial image |
description |
The problem of automated urban road network extraction is extremely complex because roads in urban scenes strongly interact with other objects. This problem can be simplified if road regions are first isolated using a classification procedure. The isolated road regions can be posteriorly used in tasks of refinement and reconstruction of the road network. This article addresses only the problem of road regions’ detection using Artificial Neural Network as classification method. However, in urban areas, the use of spectral data alone commonly leads to the confusion of the road class with other classes in RGB images, such as building roofs and concrete, because these objects may present similar spectral characteristics. To overcome this problem, it is proposed the integration of a high-resolution RGB aerial image with laser-derived images. The classification results showed that the integration of the geometric (height) and radiometric (laser pulse intensity) laser data significantly improved the classification accuracy, also contributing for the better detection of road pixel. The laser intensity data help to overcome the effects of road obstructions caused by shadows and trees. On the other hand, the laser height data help to separate the aboveground objects from those on the ground level. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:24:06Z 2018-12-11T17:24:06Z 2018-05-05 |
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/10.1080/19479832.2018.1469547 International Journal of Image and Data Fusion, p. 1-21. 1947-9824 1947-9832 http://hdl.handle.net/11449/177127 10.1080/19479832.2018.1469547 2-s2.0-85046467178 2-s2.0-85046467178.pdf |
url |
http://dx.doi.org/10.1080/19479832.2018.1469547 http://hdl.handle.net/11449/177127 |
identifier_str_mv |
International Journal of Image and Data Fusion, p. 1-21. 1947-9824 1947-9832 10.1080/19479832.2018.1469547 2-s2.0-85046467178 2-s2.0-85046467178.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal of Image and Data Fusion 0,697 0,697 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1-21 application/pdf |
dc.source.none.fl_str_mv |
Scopus 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_ |
1803045478549422080 |