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dc.contributor.authorTaşar, Onuren_US
dc.contributor.authorAksoy, Selimen_US
dc.coverage.spatialBeijing, China
dc.date.accessioned2018-04-12T11:49:26Z
dc.date.available2018-04-12T11:49:26Z
dc.date.issued2016-07en_US
dc.identifier.urihttp://hdl.handle.net/11693/37731
dc.descriptionDate of Conference: 10-15 July 2016
dc.descriptionConference name: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016
dc.description.abstractFusion of aerial optical and LiDAR data has been a popular problem in remote sensing as they carry complementary information for object detection. We describe a stratified method that involves separately thresholding the normalized digital surface model derived from LiDAR data and the normalized difference vegetation index derived from spectral bands to obtain candidate image parts that contain different object classes, and incorporates spectral and height data with spatial information in a graph cut framework to segment the rest of the image where such separation is not possible. Experiments using a benchmark data set show that the performance of the proposed method that uses small amount of supervision is compatible with the ones in the literature. © 2016 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleInternational Geoscience and Remote Sensing Symposium, (IGARSS) 2016en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/IGARSS.2016.7730879en_US
dc.subjectData fusionen_US
dc.subjectGraph cuten_US
dc.subjectObject detectionen_US
dc.subjectBuildings
dc.subjectVegetation mapping
dc.subjectLaser radar
dc.subjectOptical imaging
dc.subjectOptical sensors
dc.subjectEigenvalues and eigenfunctions
dc.subjectVegetation
dc.titleObject detection using optical and LiDAR data fusionen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage7204en_US
dc.citation.epage7207en_US
dc.identifier.doi10.1109/IGARSS.2016.7730879en_US
dc.publisherIEEEen_US


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