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dc.contributor.authorLi, Er
dc.contributor.authorFemiani, John
dc.contributor.authorXu, Shibiao
dc.contributor.authorZhang, Xiaopeng
dc.contributor.authorWonka, Peter
dc.date.accessioned2015-08-03T12:36:00Z
dc.date.available2015-08-03T12:36:00Z
dc.date.issued2015-08
dc.identifier.issn01962892
dc.identifier.doi10.1109/TGRS.2015.2400462
dc.identifier.urihttp://hdl.handle.net/10754/564195
dc.description.abstractIn this paper, we propose a robust framework for building extraction in visible band images. We first get an initial classification of the pixels based on an unsupervised presegmentation. Then, we develop a novel conditional random field (CRF) formulation to achieve accurate rooftops extraction, which incorporates pixel-level information and segment-level information for the identification of rooftops. Comparing with the commonly used CRF model, a higher order potential defined on segment is added in our model, by exploiting region consistency and shape feature at segment level. Our experiments show that the proposed higher order CRF model outperforms the state-of-the-art methods both at pixel and object levels on rooftops with complex structures and sizes in challenging environments. © 1980-2012 IEEE.
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China under Grant 61331018, Grant 91338202, and Grant 61100132.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectBuildings
dc.subjectrooftops conditional random field (CRF)
dc.subjectshadows
dc.titleRobust rooftop extraction from visible band images using higher order CRF
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journalIEEE Transactions on Geoscience and Remote Sensing
dc.contributor.institutionDepartment of Engineering and Computing Systems, Arizona State UniversityMesa, AZ, United States
dc.contributor.institutionNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China
dc.contributor.institutionDepartment of Computer Science and Engineering, Arizona State UniversityTempe, AZ, United States
kaust.personWonka, Peter


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