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Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes

dc.creatorLee, Juheon
dc.creatorCai, Xiaohao
dc.creatorSchönlieb, Carola-Bibiane
dc.creatorCoomes, David Anthony
dc.date.accessioned2018-11-24T23:18:13Z
dc.date.available2015-06-17T13:57:11Z
dc.date.available2018-11-24T23:18:13Z
dc.date.issued2015-06-02
dc.identifierhttps://www.repository.cam.ac.uk/handle/1810/248530
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/123456789/3232
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.languageen
dc.publisherIEEE
dc.publisherIEEE Transactions on Geoscience and Remote Sensing
dc.rightshttp://creativecommons.org/licenses/by/2.0/uk/
dc.rightsAttribution 2.0 UK: England & Wales
dc.subjectaerial photograph
dc.subjecthyperspectral image
dc.subjectimage registration
dc.subjectlight detecting and ranging (LiDAR)
dc.subjectremote sensing
dc.titleNon-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes
dc.typeArticle


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