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dc.contributor.authorSiddiqui, Fasahat Ullah
dc.contributor.authorTeng, Shyh Wei
dc.contributor.authorLu, Guojun
dc.contributor.authorAwrangjeb, Mohammad
dc.contributor.editorRhee, T
dc.contributor.editorRayudu, R
dc.contributor.editorHollitt, C
dc.contributor.editorLewis, J
dc.contributor.editorZhang, M
dc.date.accessioned2018-01-08T01:30:46Z
dc.date.available2018-01-08T01:30:46Z
dc.date.issued2013
dc.identifier.isbn978-1-4799-0882-0
dc.identifier.issn2151-2191
dc.identifier.doi10.1109/IVCNZ.2013.6727060
dc.identifier.urihttp://hdl.handle.net/10072/99596
dc.description.abstractIn this paper, the height variation in LIDAR (Light Detection And Ranging) point cloud data and point density are analyzed to remove the false building detection in highly vegetation and hilly sites. In general, the LIDAR points in a tree area have higher height variations than those in a building area. Moreover, the density of points having similar height values is lower in a tree area than in a building area. The proposed method uses such information as an improvement to a current state-of-the-art building detection method. The qualitative and object-based quantitative analyzes have been performed to verify the effectiveness of the proposed building detection method as compared with a current method. The analysis shows that proposed building detection method successfully reduces false building detection (i.e. trees in high complex sites of Australia and Germany), and the average correctness and quality have been improved by 6.36% and 6.16% respectively.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencename28th International Conference on Image and Vision Computing New Zealand (IVCNZ)
dc.relation.ispartofconferencetitlePROCEEDINGS OF 2013 28TH INTERNATIONAL CONFERENCE ON IMAGE AND VISION COMPUTING NEW ZEALAND (IVCNZ 2013)
dc.relation.ispartofdatefrom2013-11-27
dc.relation.ispartofdateto2013-11-29
dc.relation.ispartoflocationWellington, NEW ZEALAND
dc.relation.ispartofpagefrom471
dc.relation.ispartofpageto476
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchPhotogrammetry and Remote Sensing
dc.subject.fieldofresearchcode080106
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode090905
dc.titleAn improved building detection in complex sites using the LIDAR height variation and point density
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionPost-print
gro.rights.copyright© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorAwrangjeb, Mohammad


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