Busca avançada
Ano de início
Entree


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

Texto completo
Autor(es):
dos Santos, R. C. ; Galo, M. ; Habib, A. F. ; Yilmaz, A ; Wegner, JD ; Qin, R ; Remondino, F ; Fuse, T ; Toschi, I
Número total de Autores: 9
Tipo de documento: Artigo Científico
Fonte: XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III; v. 5-2, p. 8-pg., 2022-01-01.
Resumo

Building 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 F-score around 97%. (AU)

Processo FAPESP: 20/12481-7 - Regularização de contornos de telhados de edificações usando CD-spline a partir de dados LiDAR
Beneficiário:Renato César dos Santos
Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado
Processo FAPESP: 19/05268-8 - REGULARIZAÇÃO DE CONTORNOS DE TELHADOS DE EDIFICAÇÕES A PARTIR DA INTEGRAÇÃO DE DADOS LiDAR E IMAGENS AÉREAS UTILIZANDO O CONCEITO CD-SPLINE
Beneficiário:Renato César dos Santos
Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado