Road region detection in urban areas combining high-resolution RGB image and laser scanning data in a classification framework

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorPoz, A.P. Dal-
Autor(es): dc.creatorMendes, T. S.G.-
Data de aceite: dc.date.accessioned2025-08-21T20:50:18Z-
Data de disponibilização: dc.date.available2025-08-21T20:50:18Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2013-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/227944-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/227944-
Descrição: dc.descriptionThis paper addresses the problem of road region detection in urban areas using an image classification approach. In order to minimize the spectral superposition of the road (asphalt) class with other classes, the Artificial Neural Networks (ANN) image classification method was used to classify geometrically-integrated high-resolution RGB aerial and laser-derived images. The RGB image was combined with different laser data layers and the ANN classification results showed that the radiometric and geometric laser data allows a better detection of road pixel.-
Descrição: dc.descriptionDept. of Cartography, São Paulo State University, R. Roberto Simonsen, 305-
Descrição: dc.descriptionDept. of Cartography, São Paulo State University, R. Roberto Simonsen, 305-
Formato: dc.format53-56-
Idioma: dc.languageen-
Relação: dc.relationInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial Neural Network-
Palavras-chave: dc.subjectLaser pulse intensity image-
Palavras-chave: dc.subjectNormalized digital surface model-
Palavras-chave: dc.subjectRGB aerial image-
Título: dc.titleRoad region detection in urban areas combining high-resolution RGB image and laser scanning data in a classification framework-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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