Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data

Detalhes bibliográficos
Autor(a) principal: Dinis, J.
Data de Publicação: 2010
Outros Autores: Navarro, A., Soares, F., Santos, T., Freire, S., Fonseca, A. M., Afonso, N., Tenedório, J.
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.lnec.pt:8080/jspui/handle/123456789/1001472
Resumo: In Portugal, updating municipal plans (1:10 000) is required every ten years. High spatial resolution imagery has shown its potential for detailed urban land cover mapping at large scales. However, shadows are a major problem in those images and especially in the case of urban environments. The purpose of this study is to develop a less time consuming and less expensive alternative approach to the traditional geographic data extraction for municipal plans production. A hierarchical object oriented classification method, that combines a multitemporal data set of high resolution satellite imagery and Light Detection And Ranging (LiDAR) data, is presented for the Municipality of Lisbon. A histogram thresholding method and a Spectral Shape Index (SSI) are initially applied to discriminate shadowed from non-shadowed objects using a 2007 QuickBird image. These non-shadowed objects are then divided into vegetated and non-vegetated objects using a Normalized Difference Vegetation Index (NDVI). Through a rule-based classification using the height information from LiDAR data, vegetated objects are classified into grassland, shrubs and trees while non-vegetated objects are distinguished into low and high features. Low features are then separated into bare soil and transport units, again using a NDVI, while high features are classified as buildings and high crossroads using the shape of the objects (density). The 2007 shadowed objects are classified based on the spectral and spatial information of a 2005 QuickBird image, where shadows are in different directions. The developed methodology produced results with an overall accuracy of 87%. Misclassifications among vegetated features are due to the fact that the nDSM did not express the height for permeable features, while among non-vegetated features are due to temporal discrepancies between the DTM and the DSM, to different satellite azimuths in the 2005 and 2007 images and to unsuitable contextual rules.
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spelling Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation dataQuickbirdObject-oriented classificationLidarIn Portugal, updating municipal plans (1:10 000) is required every ten years. High spatial resolution imagery has shown its potential for detailed urban land cover mapping at large scales. However, shadows are a major problem in those images and especially in the case of urban environments. The purpose of this study is to develop a less time consuming and less expensive alternative approach to the traditional geographic data extraction for municipal plans production. A hierarchical object oriented classification method, that combines a multitemporal data set of high resolution satellite imagery and Light Detection And Ranging (LiDAR) data, is presented for the Municipality of Lisbon. A histogram thresholding method and a Spectral Shape Index (SSI) are initially applied to discriminate shadowed from non-shadowed objects using a 2007 QuickBird image. These non-shadowed objects are then divided into vegetated and non-vegetated objects using a Normalized Difference Vegetation Index (NDVI). Through a rule-based classification using the height information from LiDAR data, vegetated objects are classified into grassland, shrubs and trees while non-vegetated objects are distinguished into low and high features. Low features are then separated into bare soil and transport units, again using a NDVI, while high features are classified as buildings and high crossroads using the shape of the objects (density). The 2007 shadowed objects are classified based on the spectral and spatial information of a 2005 QuickBird image, where shadows are in different directions. The developed methodology produced results with an overall accuracy of 87%. Misclassifications among vegetated features are due to the fact that the nDSM did not express the height for permeable features, while among non-vegetated features are due to temporal discrepancies between the DTM and the DSM, to different satellite azimuths in the 2005 and 2007 images and to unsuitable contextual rules.GEOBIA 20102011-01-05T11:08:02Z2014-10-09T13:51:24Z2017-04-12T16:10:17Z2010-06-29T00:00:00Z2010-06-29conference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://repositorio.lnec.pt:8080/jspui/handle/123456789/1001472engDinis, J.Navarro, A.Soares, F.Santos, T.Freire, S.Fonseca, A. M.Afonso, N.Tenedório, J.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-09-28T03:03:09Zoai:localhost:123456789/1001472Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-09-28T03:03:09Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data
title Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data
spellingShingle Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data
Dinis, J.
Quickbird
Object-oriented classification
Lidar
title_short Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data
title_full Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data
title_fullStr Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data
title_full_unstemmed Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data
title_sort Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data
author Dinis, J.
author_facet Dinis, J.
Navarro, A.
Soares, F.
Santos, T.
Freire, S.
Fonseca, A. M.
Afonso, N.
Tenedório, J.
author_role author
author2 Navarro, A.
Soares, F.
Santos, T.
Freire, S.
Fonseca, A. M.
Afonso, N.
Tenedório, J.
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Dinis, J.
Navarro, A.
Soares, F.
Santos, T.
Freire, S.
Fonseca, A. M.
Afonso, N.
Tenedório, J.
dc.subject.por.fl_str_mv Quickbird
Object-oriented classification
Lidar
topic Quickbird
Object-oriented classification
Lidar
description In Portugal, updating municipal plans (1:10 000) is required every ten years. High spatial resolution imagery has shown its potential for detailed urban land cover mapping at large scales. However, shadows are a major problem in those images and especially in the case of urban environments. The purpose of this study is to develop a less time consuming and less expensive alternative approach to the traditional geographic data extraction for municipal plans production. A hierarchical object oriented classification method, that combines a multitemporal data set of high resolution satellite imagery and Light Detection And Ranging (LiDAR) data, is presented for the Municipality of Lisbon. A histogram thresholding method and a Spectral Shape Index (SSI) are initially applied to discriminate shadowed from non-shadowed objects using a 2007 QuickBird image. These non-shadowed objects are then divided into vegetated and non-vegetated objects using a Normalized Difference Vegetation Index (NDVI). Through a rule-based classification using the height information from LiDAR data, vegetated objects are classified into grassland, shrubs and trees while non-vegetated objects are distinguished into low and high features. Low features are then separated into bare soil and transport units, again using a NDVI, while high features are classified as buildings and high crossroads using the shape of the objects (density). The 2007 shadowed objects are classified based on the spectral and spatial information of a 2005 QuickBird image, where shadows are in different directions. The developed methodology produced results with an overall accuracy of 87%. Misclassifications among vegetated features are due to the fact that the nDSM did not express the height for permeable features, while among non-vegetated features are due to temporal discrepancies between the DTM and the DSM, to different satellite azimuths in the 2005 and 2007 images and to unsuitable contextual rules.
publishDate 2010
dc.date.none.fl_str_mv 2010-06-29T00:00:00Z
2010-06-29
2011-01-05T11:08:02Z
2014-10-09T13:51:24Z
2017-04-12T16:10:17Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.lnec.pt:8080/jspui/handle/123456789/1001472
url http://repositorio.lnec.pt:8080/jspui/handle/123456789/1001472
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv GEOBIA 2010
publisher.none.fl_str_mv GEOBIA 2010
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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