K-MEANS CLUSTERING BASED ON OMNIVARIANCE ATTRIBUTE FOR BUILDING DETECTION FROM AIRBORNE LIDAR DATA

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorPurdue University-
Autor(es): dc.creatorDos Santos, R. C.-
Autor(es): dc.creatorGalo, M.-
Autor(es): dc.creatorHabib, A. F.-
Data de aceite: dc.date.accessioned2025-08-21T22:08:05Z-
Data de disponibilização: dc.date.available2025-08-21T22:08:05Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2022-05-17-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5194/isprs-annals-V-2-2022-111-2022-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241185-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241185-
Descrição: dc.descriptionBuilding detection is an important process in urban applications. In the last decades, 3D point clouds derived from airborne LiDAR have been widely explored. In this paper, we propose a building detection method based on K-means clustering and the omnivariance attribute derived from eigenvalues. The main contributions lie on the automatic detection without the need for training and optimal neighborhood definition for local attribute estimation. Additionally, one refinement step based on mathematical morphology (MM) operators to minimize the classification errors (commission and omission errors) is proposed. The experiments were conducted in three study areas. In general, the results indicated the potential of proposed method, presenting an average Fscore around 97%.-
Descrição: dc.descriptionSão Paulo State University - Unesp Dept. Of Cartography Presidente Prudente-
Descrição: dc.descriptionLyles School Of Civil Engineering Purdue University-
Descrição: dc.descriptionSão Paulo State University - Unesp Dept. Of Cartography Presidente Prudente-
Formato: dc.format111-118-
Idioma: dc.languageen-
Relação: dc.relationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAirborne LiDAR-
Palavras-chave: dc.subjectBuilding Detection-
Palavras-chave: dc.subjectClustering-
Palavras-chave: dc.subjectGeometric Feature-
Palavras-chave: dc.subjectMathematical Morphology-
Título: dc.titleK-MEANS CLUSTERING BASED ON OMNIVARIANCE ATTRIBUTE FOR BUILDING DETECTION FROM AIRBORNE LIDAR DATA-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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