Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov

Automatic extraction of building roof contours by laser scanning data and markov random field

dc.contributorUniv Estado Mato Grosso
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.creatorDos Santos Galvanin, Edineia Aparecida [UNESP]
dc.creatorDal Poz, Aluir Porfírio [UNESP]
dc.creatorPires de Souza, Aparecida Doniseti [UNESP]
dc.date2014-05-20T13:22:42Z
dc.date2014-05-20T13:22:42Z
dc.date2008-01-01
dc.identifierhttp://ojs.c3sl.ufpr.br/ojs2/index.php/bcg/article/view/11817
dc.identifierBoletim de Ciências Geodesicas. Curitiba Pr: Universidade Federal do Paraná (UFPR), Centro Politecnico, v. 14, n. 2, p. 221-241, 2008.
dc.identifier1413-4853
dc.identifierhttp://hdl.handle.net/11449/6697
dc.identifierWOS:000260626000005
dc.identifierWOS000260626000005.pdf
dc.identifier2628413289391037
dc.descriptionThis paper proposes a methodology for automatic extraction of building roof contours from a Digital Elevation Model (DEM), which is generated through the regularization of an available laser point cloud. The methodology is based on two steps. First, in order to detect high objects (buildings, trees etc.), the DEM is segmented through a recursive splitting technique and a Bayesian merging technique. The recursive splitting technique uses the quadtree structure for subdividing the DEM into homogeneous regions. In order to minimize the fragmentation, which is commonly observed in the results of the recursive splitting segmentation, a region merging technique based on the Bayesian framework is applied to the previously segmented data. The high object polygons are extracted by using vectorization and polygonization techniques. Second, the building roof contours are identified among all high objects extracted previously. Taking into account some roof properties and some feature measurements (e. g., area, rectangularity, and angles between principal axes of the roofs), an energy function was developed based on the Markov Random Field (MRF) model. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM's showed that the methodology works properly, as it delivered roof contours with approximately 90% shape accuracy and no false positive was verified.
dc.descriptionUniv Estado Mato Grosso, Dept Matemat, BR-78390000 Barra do Bugres, MT, Brazil
dc.descriptionUniv Estadual Paulista, Fac Ciencias & Tecnol, BR-19060900 Presidente Prudente, SP, Brazil
dc.descriptionUniv Estadual Paulista, Programa Posgrad Ciencias Cartograf, BR-19060900 Presidente Prudente, SP, Brazil
dc.descriptionUniv Estadual Paulista, Fac Ciencias & Tecnol, BR-19060900 Presidente Prudente, SP, Brazil
dc.descriptionUniv Estadual Paulista, Programa Posgrad Ciencias Cartograf, BR-19060900 Presidente Prudente, SP, Brazil
dc.format221-241
dc.languagepor
dc.publisherUniversidade Federal do Paraná (UFPR), Centro Politecnico
dc.relationBoletim de Ciências Geodésicas
dc.relation240677
dc.relation0,188
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectAutomatic Extraction
dc.subjectBuilding Roof Contours
dc.subjectDigital Elevation Model
dc.subjectLaser Scanning Data
dc.subjectMarkov Random Field
dc.titleExtração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov
dc.titleAutomatic extraction of building roof contours by laser scanning data and markov random field
dc.typeArtigo


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