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Nonparametric image registration of airborne LiDAR, hyperspectral and photographic imagery of wooded landscapes

Nonparametric image registration of airborne LiDAR, hyperspectral and photographic imagery of wooded landscapes
Nonparametric image registration of airborne LiDAR, hyperspectral and photographic imagery of wooded landscapes
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 detection and ranging (LiDAR), remote sensing
0196-2892
6073-6084
Lee, Juheon
cd382ebf-0bcc-47b8-a60d-68c6540d31bb
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Schönlieb, Carola Bibiane
655fcff7-df67-4700-ac57-c318656c4722
Coomes, David A.
4e3d573c-fda0-4ddc-a621-6a682ff615ca
Lee, Juheon
cd382ebf-0bcc-47b8-a60d-68c6540d31bb
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Schönlieb, Carola Bibiane
655fcff7-df67-4700-ac57-c318656c4722
Coomes, David A.
4e3d573c-fda0-4ddc-a621-6a682ff615ca

Lee, Juheon, Cai, Xiaohao, Schönlieb, Carola Bibiane and Coomes, David A. (2015) Nonparametric image registration of airborne LiDAR, hyperspectral and photographic imagery of wooded landscapes. IEEE Transactions on Geoscience and Remote Sensing, 53 (11), 6073-6084. (doi:10.1109/TGRS.2015.2431692).

Record type: Article

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.

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More information

e-pub ahead of print date: 2 June 2015
Published date: 1 November 2015
Keywords: Aerial photograph, hyperspectral image, image registration, light detection and ranging (LiDAR), remote sensing

Identifiers

Local EPrints ID: 440813
URI: http://eprints.soton.ac.uk/id/eprint/440813
ISSN: 0196-2892
PURE UUID: b64d53b4-9004-4f61-ab40-41ff39c9264a

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Date deposited: 19 May 2020 17:02
Last modified: 19 May 2020 17:02

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Contributors

Author: Juheon Lee
Author: Xiaohao Cai
Author: Carola Bibiane Schönlieb
Author: David A. Coomes

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