Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/11858
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dc.contributor.authorSkarlatos, Dimitrios-
dc.contributor.authorVlachos, Marinos-
dc.date.accessioned2018-07-09T11:04:45Z-
dc.date.available2018-07-09T11:04:45Z-
dc.date.issued2018-05-28-
dc.identifier.citationISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020", 2018, Riva del Garda, Italy, 4–7 Juneen_US
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/11858-
dc.description.abstractCurrent advancements on photogrammetric software along with affordability and wide spreading of Unmanned Aerial Vehicles (UAV), allow for rapid, timely and accurate 3D modelling and mapping of small to medium sized areas. Although the importance and applications of large format aerial overlaps cameras and photographs in Digital Surface Model (DSM) production and LIDAR data is well documented in literature, this is not the case for UAV photography. Additionally, the main disadvantage of photogrammetry is the inability to map the dead ground (terrain), when we deal with areas that include vegetation. This paper assesses the use of near-infrared imagery captured by small UAV platforms to automatically remove vegetation from Digital Surface Models (DSMs) and obtain a Digital Terrain Model (DTM). Two areas were tested, based on the availability of ground reference points, both under trees and among vegetation, as well as on terrain. In addition, RGB and near-infrared UAV photography was captured and processed using Structure from Motion (SfM) and Multi View Stereo (MVS) algorithms to generate DSMs and corresponding colour and NIR orthoimages with 0.2 m and 0.25 m as pixel size respectively for the two test sites. Moreover, orthophotos were used to eliminate the vegetation from the DSMs using NDVI index, thresholding and masking. Following that, different interpolation algorithms, according to the test sites, were applied to fill in the gaps and created DTMs. Finally, a statistic analysis was made using reference terrain points captured on field, both on dead ground and under vegetation to evaluate the accuracy of the whole process and assess the overall accuracy of the derived DTMs in contrast with the DSMs.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© Authors 2018en_US
dc.subjectDSMen_US
dc.subjectDTMen_US
dc.subjectNear infrareden_US
dc.subjectVegetation removalen_US
dc.titleVegetation removal from UAV derived DSMS, using combination of RGB and NIR imageryen_US
dc.typeConference Papersen_US
dc.doihttps://doi.org/10.5194/isprs-annals-IV-2-255-2018en_US
dc.subject.categoryCivil Engineeringen_US
dc.subject.categoryCivil Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
cut.common.academicyear2017-2018en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1other-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-2732-4780-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers
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