Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes
IEEE Transactions on Geoscience and Remote Sensing
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Lee, J., Cai, X., Schönlieb, C., & Coomes, D. (2015). Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes. IEEE Transactions on Geoscience and Remote Sensing, 53 6073-6084. https://doi.org/10.1109/TGRS.2015.2431692
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.
aerial photograph, hyperspectral image, image registration, light detecting and ranging (LiDAR), remote sensing
This 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.
Alan Turing Institute (unknown)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)
External DOI: https://doi.org/10.1109/TGRS.2015.2431692
This record's URL: https://www.repository.cam.ac.uk/handle/1810/248530
Attribution 2.0 UK: England & Wales
Licence URL: http://creativecommons.org/licenses/by/2.0/uk/
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