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
2. BACKGROUND
TLS has become an established method for obtaining 3D
geometry in many applications, such as architecture, mining,
geology, and the wider earth sciences. In these disciplines,
the method has become attractive, due to the rapid data
acquisition, the high point precision and the ease of use,
which has resulted in the technique being adopted by domain
experts outside the geomatics field. Because laser scanning is
an active technique, the strength of the returned laser pulse,
commonly referred to as intensity or amplitude, is recorded
(Hófle and Pfeifer, 2007). Intensity gives some benefit for
object discrimination, as it is based on the absorption and
reflectance properties of the measured material at the
wavelength of the laser beam used (Bellian et al., 2005;
Franceschi et al., 2009; Nield et al., 2011). However, as the
laser operates within a very narrow spectral range, restricted
to a single band, often in the near-infrared, the intensity
values may only be practical for differentiating major
material differences (Kurz et al., 2011).
To obtain a greater spectral resolution, a number of studies
have used multispectral approaches. Lichti (2005) classified
laser scanned scenes using the true colour (red, green, blue:
RGB) channel and single infrared band of the used
instrument. Hemmleb et al. (2006) developed a multispectral
laser scanner, by combining several laser sources at different
wavelengths, for studying building damage. Most recently
close range hyperspectral sensors have been used to separate
a wider range of materials, such as spectrally similar rock
types (Kurz et al., 2012).
It is common for digital imagery to be collected
simultaneously with laser scans. A number of instruments
integrate a digital camera into the acquisition pipeline, giving
access to calibrated semi-metric imagery of high resolution
(e.g. Buckley et al, 2008). Though a point cloud may be
displayed using the intensity value for colour, the inherently
sparse, or discontinuous, data can make detailed
interpretation difficult. Features may have a minimal trace in
3D, but may be obvious in 2D images as colour or edges. For
this reason, photorealistic modelling, combining surface data
and imagery, is a standard approach. Converting a lidar point
cloud to a triangular mesh makes the surface representation
continuous, and colour information from digital photos can
be texture mapped to create an integrated product (El-Hakim
et al., 1998; Frueh et al., 2005). Such a photorealistic model
offers value for visualisation, education and quantitative
measurement of recorded objects (e.g. Bellian et al., 2005).
Despite the advantages of combining terrestrial laser and
image data in photorealistic models, little quantitative
information is provided on the mineral and chemical
composition of the scanned object. This lack provides the
motivation for integrating a further data type, close range
hyperspectral imagery, into the modelling workflow, with the
associated benefits of an extended spectral range and
resolution.
3. DATASET AND PROCESSING WORKFLOW
3.1 Application
Geology is one application field where terrestrial laser
scanning integrated with conventional visible light digital
542
imaging has become widespread (Bellian et al, 2005;
Buckley et al., 2008; Buckley et al., 2010). Photorealistic
models are created of geological outcrops, allowing both
surface topography and geological features to be studied in
detail. Because well-exposed outcrops often comprise high
and steep cliff sections and quarry faces, non-contact
measurement techniques are essential. While mineralogical
study has been possible in the past using limited spot
samples, close range hyperspectral imaging offers a remote
and high resolution means of analysing chemical distribution
over larger areas. Example data from geological outcrop
studies are used here to illustrate the potential benefits of
combined TLS and hyperspectral scanning.
3.2 Instrumentation and data collection
Data were collected from two sites, the first a cliff section at
Garley Canyon, Utah, USA, the second a quarry wall at
Pozalagua, Cantabria, Spain (Fig. 1). A Riegl LMS-Z420i
terrestrial laser scanner was used to collect point clouds of
the two outcrops, and a calibrated Nikon D200 camera with a
Nikkor 85 mm lens, mounted on top of the scanner, provided
imagery registered in the lidar coordinate system. Several
scans were collected per outcrop to avoid shadows caused by
obstruction from the individual instrument positions (two for
Garley Canyon and seven at Pozalagua). A global navigation
satellite system (GNSS) was used to record the position of
each scan position (Buckley et al., 2010).
Figure 1. Outcrops at Garley Canyon (top) and the Pozalagua
Quarry (bottom).
A HySpex SWIR-320m hyperspectral imager (Norsk Elektro
Optikk, 2012) was used to image each of the two outcrops.
The HySpex imager records 240 bands across the short-wave
infrared (SWIR) part of the electromagnetic spectrum, with a
spectral sampling of 5 nm. The spatial dimension of the
sensor contains 320 pixels, effectively resulting in line
(pushbroom) scanning when used with a rotation stage.
Because rotation is required to build up the number of image
columns, the width of an image is variable, determined by the
user. This configuration results in panoramic imaging
geometry, where only the across-track image direction can be
represented by central perspective projection (Luhmann et
al., 2006; Schneider and Maas, 2006). Spatial resolution is
low compared with a contemporary digital camera, but the
high spectral resolution allows greater differentiation of
mineral content. In this study, multiple images were collected
for each area, and one representative image was selected for
hyperspectral processing and data fusion for both sites.
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