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Technical Commission VII

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
S. J. Buckley", T. H. Kurz*, D. Schneider"
“Uni CIPR, Postboks 7810, N-5020 Bergen, Norway - simon.buckley@uni.no, tobias.kurz@uni.no
Technische Universität Dresden, Institute of Photogrammetry and Remote Sensing, D-01062 Dresden, Germany —
TS VII/3, VII/6, I11/2, V/3: Integration of hyperspectral and lidar data
KEY WORDS: Hyper spectral, lidar, fusion, visualization, analysis, texture, value-added, photo-realism
Close range hyperspectral imaging is a developing method for the analysis and identification of material composition in many
applications, such as in within the earth sciences. Using compact imaging devices in the field allows near-vertical topography to be
imaged, thus bypassing the key limitations of viewing angle and resolution that preclude the use of airborne and spaceborne
platforms. Terrestrial laser scanning allows 3D topography to be captured with high precision and spatial resolution. The
combination of 3D geometry from laser scanning, and material properties from hyperspectral imaging allows new fusion products to
be created, adding new information for solving application problems. This paper highlights the advantages of terrestrial lidar and
hyperspectral integration, focussing on the qualitative and quantitative aspects, with examples from a geological field application.
Accurate co-registration of the two data types is required. This allows 2D pixels to be linked to the 3D lidar geometry, giving
increased quantitative analysis as classified material vectors are projected to 3D space for calculation of areas and examination of
spatial relationships. User interpretation of hyperspectral results in a spatially-meaningful manner is facilitated using visual methods
that combine the geometric and mineralogical products in a 3D environment. Point cloud classification and the use of photorealistic
modelling enhance qualitative validation and interpretation, and allow image registration accuracy to be checked. A method for
texture mapping of lidar meshes with multiple image textures, both conventional digital photos and hyperspectral results, is
described. The integration of terrestrial laser scanning and hyperspectral imaging is a valuable means of providing new analysis
methods, suitable for many applications requiring linked geometric and chemical information.
1. INTRODUCTION Compact and portable hyperspectral sensors are now
available, and are applicable to many close range
Applications in photogrammetry and remote sensing are often applications, especially in the earth sciences, where the
enhanced by the use of multiple sensors that provide remote analysis of mineralogical distribution is often
complementary information and therefore improved products important. Such sensors have high spectral resolution,
for users. A common example is the combination of laser allowing increasingly complex mineral variations to be
scanning and photogrammetry, both terrestrial and aerial, detected (Kurz et al, 2012). In addition, the close range
where the high density and precise geometric data from lidar instrument position makes it possible to image steep terrain
is given added value using the increased radiometric, spectral and building façades, without the traditional problems
and spatial resolution provided by digital metric or semi- associated with airborne and spaceborne imaging where
metric imagery (e.g. Axelsson, 1999; Frueh et al., 2005; Fassi spatial resolution and a nadir field of view are problematic.
et al., 2011; Guo et al., 2011). Multispectral or hyperspectral The integration of hyperspectral imagery with terrestrial laser
sensors have the potential to further complement laser scanning (TLS) has high potential for assisting application
scanning topographic data, as an extended part of the experts with interpretation and validation of classification
electromagnetic spectrum is recorded with a greater number results, owing to the improved spatial component provided
of spectral bands. Such sensors measure many narrow bands, by linking 2D spectral data to 3D lidar geometry.
allowing near-continuous reflectance curves to be derived per
component image pixel (van der Meer and de Jong, 2001). This paper assesses the benefits of integrating TLS data and
Such high spectral sampling makes it possible to analyse close range hyperspectral imagery, focussing on the
small variations in material composition, even at the sub- combined products obtainable from these complementary
pixel range (Keshava and Mustard, 2002). While the value of techniques. A geological application is used to illustrate the
combined airborne lidar and hyperspectral data, for example proposed data fusion products. The paper is structured to
in forestry, is being realised (Koch, 2010), in close range give a background to the component techniques and the
applications the integration is only now emerging as a multi-sensor integration (Section 2), before the data
potential means of linking geometrical and material collection, processing and registration is described (Section
properties (Kurz et al., 2008). 3). Section 4 outlines visual methods for exploring the
combined data, whilst Section 5 covers quantitative methods
and accuracy assessment.

" Corresponding author.