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Nonparametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes

Lee, J; Cai, X; Schonlieb, C-B; Coomes, DA; (2015) Nonparametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes. IEEE Transactions on Geoscience and Remote Sensing , 53 (11) pp. 6073-6084. 10.1109/TGRS.2015.2431692. Green open access

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Abstract

There 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.

Type: Article
Title: Nonparametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TGRS.2015.2431692
Publisher version: http://doi.org/10.1109/TGRS.2015.2431692
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Keywords: Aerial photograph, hyperspectral image, image registration, light detection and ranging (LiDAR), remote sensing
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics
URI: https://discovery.ucl.ac.uk/id/eprint/10025797
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