Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/43364
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dc.contributor.authorPradhan, Biswajeeten
dc.contributor.authorAhmed, Ahmed Aen
dc.contributor.authorChakraborty, Subrataen
dc.contributor.authorAlamri, Abdullahen
dc.contributor.authorLee, Chang-Wooken
dc.date.accessioned2022-02-22T03:54:59Z-
dc.date.available2022-02-22T03:54:59Z-
dc.date.issued2021-10-16-
dc.identifier.citationJournal of Sensors, v.2021, p. 1-12en
dc.identifier.issn1687-7268en
dc.identifier.issn1687-725Xen
dc.identifier.urihttps://hdl.handle.net/1959.11/43364-
dc.description.abstract<p>Satellite images have been widely used to produce land use and land cover maps and to generate other thematic layers through image processing. However, images acquired by sensors onboard various satellite platforms are affected by a systematic sensor and platform-induced geometry errors, which introduce terrain distortions, especially when the sensor does not point directly at the nadir location of the sensor. To this extent, an automated processing chain of WorldView-3 image orthorectification is presented using rational polynomial coefficient (RPC) model and laser scanning data. The research is aimed at analyzing the effects of varying resolution of the digital surface model (DSM) derived from high-resolution laser scanning data, with a novel orthorectification model. The proposed method is validated on actual data in an urban environment with complex structures. This research suggests that a DSM of 0.31 m spatial resolution is optimum to achieve practical results (root-mean-square error = 0:69 m) and decreasing the spatial resolution to 20 m leads to poor results (root-mean-square error = 7:17). Moreover, orthorectifying WorldView-3 images with freely available digital elevation models from Shuttle Radar Topography Mission (SRTM) (30 m) can result in an RMSE of 7.94 m without correcting the distortions in the building. This research can improve the understanding of appropriate image processing and improve the classification for feature extraction in urban areas.</p>en
dc.languageenen
dc.publisherHindawi Limiteden
dc.relation.ispartofJournal of Sensorsen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleOrthorectification of WorldView-3 Satellite Image Using Airborne Laser Scanning Dataen
dc.typeJournal Articleen
dc.identifier.doi10.1155/2021/5273549en
dcterms.accessRightsUNE Greenen
local.contributor.firstnameBiswajeeten
local.contributor.firstnameAhmed Aen
local.contributor.firstnameSubrataen
local.contributor.firstnameAbdullahen
local.contributor.firstnameChang-Wooken
local.profile.schoolSchool of Science and Technologyen
local.profile.emailschakra3@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeUnited Kingdomen
local.identifier.runningnumber5273549en
local.format.startpage1en
local.format.endpage12en
local.identifier.scopusid85118106804en
local.peerreviewedYesen
local.identifier.volume2021en
local.access.fulltextYesen
local.contributor.lastnamePradhanen
local.contributor.lastnameAhmeden
local.contributor.lastnameChakrabortyen
local.contributor.lastnameAlamrien
local.contributor.lastnameLeeen
dc.identifier.staffune-id:schakra3en
local.profile.orcid0000-0002-0102-5424en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/43364en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleOrthorectification of WorldView-3 Satellite Image Using Airborne Laser Scanning Dataen
local.relation.fundingsourcenoteThis research was funded by the Centre for Advanced Model-ling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, and supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1A2C1085686). Also, this research is also supported by Researchers Supporting Project number RSP-2021/14, King Saud University, Riyadh, Saudi Arabia.en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorPradhan, Biswajeeten
local.search.authorAhmed, Ahmed Aen
local.search.authorChakraborty, Subrataen
local.search.authorAlamri, Abdullahen
local.search.authorLee, Chang-Wooken
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/099290f9-0a15-4c79-b8a0-f4f6290e22bben
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2021en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/099290f9-0a15-4c79-b8a0-f4f6290e22bben
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/099290f9-0a15-4c79-b8a0-f4f6290e22bben
local.subject.for2020460106 Spatial data and applicationsen
local.subject.for2020460306 Image processingen
local.subject.for2020461103 Deep learningen
local.subject.seo2020280115 Expanding knowledge in the information and computing sciencesen
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School of Science and Technology
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