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dc.contributor.authorLee, Juheonen
dc.contributor.authorCai, Xiaohaoen
dc.contributor.authorSchönlieb, Carola-Bibianeen
dc.contributor.authorCoomes, Daviden
dc.date.accessioned2015-06-17T13:57:11Z
dc.date.available2015-06-17T13:57:11Z
dc.date.issued2015-06-02en
dc.identifier.citationLee et al. IEEE Transactions on Geoscience and Remote Sensing (2015) Vol. 53, Issue 11, pp. 6073-6084. doi: 10.1109/TGRS.2015.2431692en
dc.identifier.issn0196-2892
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/248530
dc.description.abstractThere is much current interest in using multisensor airborne remote sensing to monitor the structure and biodiversity of woodlands. This paper addresses the application of nonparametric (NP) image-registration techniques to precisely align images obtained from multisensor imaging, which is critical for the successful identification of individual trees using object recognition approaches. NP image registration, in particular, the technique of optimizing an objective function, containing similarity and regularization terms, provides a flexible approach for image registration. Here, we develop a NP registration approach, in which a normalized gradient field is used to quantify similarity, and curvature is used for regularization (NGF-Curv method). Using a survey of woodlands in southern Spain as an example, we show that NGF-Curv can be successful at fusing data sets when there is little prior knowledge about how the data sets are interrelated (i.e., in the absence of ground control points). The validity of NGF-Curv in airborne remote sensing is demonstrated by a series of experiments. We show that NGF-Curv is capable of aligning images precisely, making it a valuable component of algorithms designed to identify objects, such as trees, within multisensor data sets.
dc.description.sponsorshipThis work was supported by the Airborne Research and Survey Facility of the U.K.’s Natural Environment Research Council (NERC) for collecting and preprocessing the data used in this research project [EU11/03/100], and by the grants supported from King Abdullah University of Science Technology and Wellcome Trust (BBSRC). D. Coomes was supported by a grant from NERC (NE/K016377/1) and funding from DEFRA and the BBSRC to develop methods for monitoring ash dieback from aircraft.
dc.languageEnglishen
dc.language.isoenen
dc.publisherIEEE
dc.rightsAttribution 2.0 UK: England & Wales*
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/*
dc.subjectaerial photographen
dc.subjecthyperspectral imageen
dc.subjectimage registrationen
dc.subjectlight detecting and ranging (LiDAR)en
dc.subjectremote sensingen
dc.titleNon-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapesen
dc.typeArticle
dc.description.versionThis is the final version. It was first published by IEEE at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7116541&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_Publication_Number%3A36%29%26pageNumber%3D5.en
prism.endingPage6084
prism.publicationDate2015en
prism.publicationNameIEEE Transactions on Geoscience and Remote Sensingen
prism.startingPage6073
prism.volume53en
dc.rioxxterms.funderNERC
dc.rioxxterms.funderWellcome Trust
dc.rioxxterms.funderBBSRC
dc.rioxxterms.projectidEU11/03/100
dc.rioxxterms.projectidNE/K016377/1
rioxxterms.versionofrecord10.1109/TGRS.2015.2431692en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-06-02en
dc.contributor.orcidCoomes, David [0000-0002-8261-2582]
dc.identifier.eissn1558-0644
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/M00483X/1)
pubs.funder-project-idNERC (NE/K016377/1)
pubs.funder-project-idEPSRC (EP/J009539/1)
pubs.funder-project-idAlan Turing Institute (unknown)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)


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Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales