Hierarchical object-based classification of dense urban areas by integrating high spatial resolution satellite images and lidar elevation data
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , , , , , |
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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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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1817548528322347008 |