Extraction of building roof contours from the integration of high-resolution aerial imagery and laser data using Markov random fields

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dc.contributor.authorMarcato Fernandes, Vanessa Jordao
dc.contributor.authorDal Poz, Aluir Porfirio [UNESP]
dc.contributor.institutionFed Univ Grande Dourados
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2019-10-04T12:31:00Z
dc.date.available2019-10-04T12:31:00Z
dc.date.issued2018-01-01
dc.description.abstractThis study proposes a method for the automatic extraction of building roof contours from the integration of airborne laser scanning (ALS) and photogrammetric data using the Markov random field probabilistic approach. Initially, a normalised digital surface model (nDSM) obtained from the ALS point cloud is segmented to obtain the polygons that represent high objects in the scene. These polygons are projected onto the image to delimit regions (sub-images) that will be segmented in the image, which allows the extraction of polygons in the image that represent the corresponding high objects. The polygons that represent roof contours are identified by the optimisation of an energy function that models the geometric and contextual properties of building roof contours via the genetic algorithm. This energy function combines the polygons extracted from the nDSM and from the image. The proposed method was evaluated with real data, including high-resolution aerial images and ALS data. The experimental results showed that the proposed method works properly, exhibits few failures and has average completeness and correctness rates above 90%.en
dc.description.affiliationFed Univ Grande Dourados, Fac Agr Sci, Rodovia Dourados Itahum,Km 12, BR-79804070 Dourados, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Cartog, Presidente Prudente, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Cartog, Presidente Prudente, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2013/13138-0
dc.description.sponsorshipIdFAPESP: 2012/22332-2
dc.description.sponsorshipIdCNPq: 306842/2013-0
dc.format.extent263-286
dc.identifierhttp://dx.doi.org/10.1080/19479832.2018.1487885
dc.identifier.citationInternational Journal Of Image And Data Fusion. Abingdon: Taylor & Francis Ltd, v. 9, n. 4, p. 263-286, 2018.
dc.identifier.doi10.1080/19479832.2018.1487885
dc.identifier.issn1947-9832
dc.identifier.urihttp://hdl.handle.net/11449/184900
dc.identifier.wosWOS:000446359800001
dc.language.isoeng
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofInternational Journal Of Image And Data Fusion
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectRoof extraction
dc.subjectALS
dc.subjectaerial images
dc.subjectMRF
dc.subjectgenetic algorithm
dc.titleExtraction of building roof contours from the integration of high-resolution aerial imagery and laser data using Markov random fieldsen
dc.typeResenha
dcterms.licensehttp://journalauthors.tandf.co.uk/permissions/reusingOwnWork.asp
dcterms.rightsHolderTaylor & Francis Ltd
unesp.author.lattes4791496159878691[2]
unesp.author.orcid0000-0002-2534-1229[2]
unesp.departmentCartografia - FCTpt

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