Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/9443
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dc.contributor.authorSkarlatos, Dimitrios-
dc.contributor.authorVlachos, Marinos-
dc.contributor.authorVamvakousis, Vasilis-
dc.contributor.otherΣκαρλάτος, Δημήτριος-
dc.contributor.otherΒλάχος, Μαρίνος-
dc.contributor.otherΒαμβακούσης, Βασίλης-
dc.date.accessioned2017-02-03T11:30:55Z-
dc.date.available2017-02-03T11:30:55Z-
dc.date.issued2015-01-01-
dc.identifier.citationVideometrics, Range Imaging, and Applications XIII Conference, Munich, Germany, 22 June 2015 through 23 June 2015en_US
dc.identifier.isbn978-162841688-6-
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/9443-
dc.description.abstractCurrent advancements on photogrammetric software along with affordability and wide spreading of Autonomous Unmanned Aerial Vehicles (AUAV), allow for rapid, timely and accurate 3D modelling and mapping of small to medium sized areas. Although the importance of flight patterns and large overlaps in aerial triangulation and Digital Surface Model (DSM) production from large format aerial cameras is well documented in literature, this is not the case for AUAV photography. This paper assess DSM accuracy of models created using different flight patterns and compares them against check points and Lidar data. Three UAV flights took place, with 70%-65% forward and side overlaps, with West-East (W-E), North-South (N-S) and Northwest-Southeast (NW-SE) directions. Blocks with different flight patterns were created and processed to create raster DSM with 0.25m ground pixel size using Multi View Stereo (MVS). Using Lidar data as reference, difference maps and statistics were calculated for each block, in order to evaluate their overall accuracy. The combined scenario performed slightly better that the rest. Because of their lower spatial resolution, Lidar data prove to be an inadequate reference data set, although according to their internal vertical precision they are superior to UAV DSM. Point cloud noise from MVS, is considerable in contrast to Lidar data. A Lidar data set from a lower flying platform such as helicopter might have been a better match to low flying UAV data.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.publisherSPIEen_US
dc.rights© 2015 SPIE.en_US
dc.subjectAUAVen_US
dc.subjectComparisonen_US
dc.subjectDSM accuracyen_US
dc.subjectDSM creationen_US
dc.subjectFlight patternen_US
dc.subjectLidaren_US
dc.subjectMVSen_US
dc.subjectPoint clouden_US
dc.titleInvestigating influence of UAV flight patterns in multi-stereo view DSM accuracyen_US
dc.typeConference Papersen_US
dc.doi10.1117/12.2184888en_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryCivil Engineeringen_US
dc.subject.categoryCivil Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1other-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-2732-4780-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers
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