Terrestrial lidar and hyperspectral data fusion products for geological outcrop analysis

Simon J. Buckley, Tobias H. Kurz, John A. Howell, Danilo Schneider

Research output: Contribution to journalArticle

57 Citations (Scopus)

Abstract

Close-range hyperspectral imaging is an emerging technique for remotely mapping mineral content and distributions in inaccessible geological outcrop surfaces, allowing subtle chemical variations to be identified with high resolution and accuracy. Terrestrial laser scanning (lidar) is an established method for rapidly obtaining three-dimensional geometry, with unparalleled point density and precision. The combination of these highly complementary data types - 3D topography and surface properties - enables the production of value-added photorealistic outcrop models, adding new information that can be used for solving geological problems. This paper assesses the benefits of merging lidar and hyperspectral imaging, and presents qualitative and quantitative means of analysing the fused datasets. The integration requires an accurate co-registration, so that the 2D hyperspectral classification products can be given real measurement units. This stage is reliant on using a model that correctly describes the imaging geometry of the hyperspectral instrument, allowing image pixels and 3D points in the lidar model to be related. Increased quantitative analysis is then possible, as areas and spatial relationships can be examined by projecting classified material boundaries into 3D space. The combined data can be interpreted in a very visual manner, by colouring and texturing the lidar geometry with hyperspectral mineral maps. Because hyperspectral processing often results in several image products and classifications, these can be difficult to analyse simultaneously. A novel visualisation method is presented, where photorealistic lidar models are superimposed with multiple texture-mapped layers, allowing blending between conventional and hyperspectral imaging products to assist with interpretation and validation. The advantages and potential of the data fusion are illustrated with example outcrop data.

Original languageEnglish
Pages (from-to)249-258
Number of pages10
JournalComputers & Geosciences
Volume54
Early online date8 Feb 2013
DOIs
Publication statusPublished - Apr 2013

Fingerprint

Data fusion
Optical radar
lidar
outcrop
geometry
Geometry
Minerals
Units of measurement
Texturing
Coloring
mineral
Merging
Topography
quantitative analysis
Surface properties
visualization
pixel
Visualization
Textures
laser

Keywords

  • ground-based
  • integration
  • surface modelling
  • terrestrial laser scanning
  • virtual outcrop models
  • visualisation

ASJC Scopus subject areas

  • Information Systems
  • Computers in Earth Sciences

Cite this

Terrestrial lidar and hyperspectral data fusion products for geological outcrop analysis. / Buckley, Simon J. ; Kurz, Tobias H.; Howell, John A.; Schneider, Danilo.

In: Computers & Geosciences, Vol. 54, 04.2013, p. 249-258.

Research output: Contribution to journalArticle

Buckley, Simon J. ; Kurz, Tobias H. ; Howell, John A. ; Schneider, Danilo. / Terrestrial lidar and hyperspectral data fusion products for geological outcrop analysis. In: Computers & Geosciences. 2013 ; Vol. 54. pp. 249-258.
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abstract = "Close-range hyperspectral imaging is an emerging technique for remotely mapping mineral content and distributions in inaccessible geological outcrop surfaces, allowing subtle chemical variations to be identified with high resolution and accuracy. Terrestrial laser scanning (lidar) is an established method for rapidly obtaining three-dimensional geometry, with unparalleled point density and precision. The combination of these highly complementary data types - 3D topography and surface properties - enables the production of value-added photorealistic outcrop models, adding new information that can be used for solving geological problems. This paper assesses the benefits of merging lidar and hyperspectral imaging, and presents qualitative and quantitative means of analysing the fused datasets. The integration requires an accurate co-registration, so that the 2D hyperspectral classification products can be given real measurement units. This stage is reliant on using a model that correctly describes the imaging geometry of the hyperspectral instrument, allowing image pixels and 3D points in the lidar model to be related. Increased quantitative analysis is then possible, as areas and spatial relationships can be examined by projecting classified material boundaries into 3D space. The combined data can be interpreted in a very visual manner, by colouring and texturing the lidar geometry with hyperspectral mineral maps. Because hyperspectral processing often results in several image products and classifications, these can be difficult to analyse simultaneously. A novel visualisation method is presented, where photorealistic lidar models are superimposed with multiple texture-mapped layers, allowing blending between conventional and hyperspectral imaging products to assist with interpretation and validation. The advantages and potential of the data fusion are illustrated with example outcrop data.",
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