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A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery

A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery
A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery
Recognising individual trees within remotely sensed imagery has important applications in forest ecology and management. Several algorithms for tree delineation have been suggested, mostly based on locating local maxima or inverted basins in raster canopy height models (CHMs) derived from Light Detection And Ranging (LiDAR) data or photographs. However, these algorithms often lead to inaccurate estimates of forest stand characteristics due to the limited information content of raster CHMs. Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiDAR point cloud, and also makes use of any optical imagery available (hyperspectral imagery in our case). First, conventional methods are used to locate local maxima in the CHM and generate an initial map of trees. Second, a graph is built from the LiDAR point cloud, fused with the hyperspectral data. For computational efficiency, the feature space of hyperspectral imagery is reduced using robust PCA. Third, a multi-class normalised cut is applied to the graph, using the initial map of trees to constrain the number of clusters and their locations. Finally, recursive normalised cut is used to subdivide, if necessary, each of the clusters identified by the initial analysis. We call this approach Multiclass Cut followed by Recursive Cut (MCRC). The effectiveness of MCRC was tested using three datasets: i) NewFor, ii) a coniferous forest in the Italian Alps, and iii) a deciduous woodland in the UK. The performance of MCRC was usually superior to that of other delineation methods, and was further improved by including high-resolution optical imagery. Since MCRC delineates the entire LiDAR point cloud in 3D, it allows individual crown characteristics to be measured. By making full use of the data available, graph cut has the potential to considerably improve the accuracy of tree delineation.
cs.CV
Lee, Juheon
cd382ebf-0bcc-47b8-a60d-68c6540d31bb
Coomes, David
4e3d573c-fda0-4ddc-a621-6a682ff615ca
Schonlieb, Carola-Bibiane
a42e0ee1-9df4-41b3-ae0e-adab80249811
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Lellmann, Jan
fb63d608-5e00-4087-8023-d5ba5d7bf7cf
Dalponte, Michele
f2c62e40-39c7-42f8-8831-04bc394a8819
Malhi, Yadvinder
5b0b372b-4a85-4018-9559-903c2bbbe15d
Butt, Nathalie
9557f2ac-89f4-4754-880e-9bc64736c423
Morecroft, Mike
f8909813-b7dd-4c5a-a082-1694e3a07788
Lee, Juheon
cd382ebf-0bcc-47b8-a60d-68c6540d31bb
Coomes, David
4e3d573c-fda0-4ddc-a621-6a682ff615ca
Schonlieb, Carola-Bibiane
a42e0ee1-9df4-41b3-ae0e-adab80249811
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Lellmann, Jan
fb63d608-5e00-4087-8023-d5ba5d7bf7cf
Dalponte, Michele
f2c62e40-39c7-42f8-8831-04bc394a8819
Malhi, Yadvinder
5b0b372b-4a85-4018-9559-903c2bbbe15d
Butt, Nathalie
9557f2ac-89f4-4754-880e-9bc64736c423
Morecroft, Mike
f8909813-b7dd-4c5a-a082-1694e3a07788

Lee, Juheon, Coomes, David, Schonlieb, Carola-Bibiane, Cai, Xiaohao, Lellmann, Jan, Dalponte, Michele, Malhi, Yadvinder, Butt, Nathalie and Morecroft, Mike (2017) A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery. arXiv.

Record type: Article

Abstract

Recognising individual trees within remotely sensed imagery has important applications in forest ecology and management. Several algorithms for tree delineation have been suggested, mostly based on locating local maxima or inverted basins in raster canopy height models (CHMs) derived from Light Detection And Ranging (LiDAR) data or photographs. However, these algorithms often lead to inaccurate estimates of forest stand characteristics due to the limited information content of raster CHMs. Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiDAR point cloud, and also makes use of any optical imagery available (hyperspectral imagery in our case). First, conventional methods are used to locate local maxima in the CHM and generate an initial map of trees. Second, a graph is built from the LiDAR point cloud, fused with the hyperspectral data. For computational efficiency, the feature space of hyperspectral imagery is reduced using robust PCA. Third, a multi-class normalised cut is applied to the graph, using the initial map of trees to constrain the number of clusters and their locations. Finally, recursive normalised cut is used to subdivide, if necessary, each of the clusters identified by the initial analysis. We call this approach Multiclass Cut followed by Recursive Cut (MCRC). The effectiveness of MCRC was tested using three datasets: i) NewFor, ii) a coniferous forest in the Italian Alps, and iii) a deciduous woodland in the UK. The performance of MCRC was usually superior to that of other delineation methods, and was further improved by including high-resolution optical imagery. Since MCRC delineates the entire LiDAR point cloud in 3D, it allows individual crown characteristics to be measured. By making full use of the data available, graph cut has the potential to considerably improve the accuracy of tree delineation.

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More information

Published date: 24 January 2017
Keywords: cs.CV

Identifiers

Local EPrints ID: 438761
URI: http://eprints.soton.ac.uk/id/eprint/438761
PURE UUID: 9114bfb5-98c7-4dd1-85e8-686a41671126

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Date deposited: 24 Mar 2020 17:30
Last modified: 28 Jul 2020 16:36

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Contributors

Author: Juheon Lee
Author: David Coomes
Author: Carola-Bibiane Schonlieb
Author: Xiaohao Cai
Author: Jan Lellmann
Author: Michele Dalponte
Author: Yadvinder Malhi
Author: Nathalie Butt
Author: Mike Morecroft

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