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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
5.1 Classification vectors
Classification algorithms result in the assignment of pixels to
different material classes, which are represented by 2D image
space vectors in remote sensing software. Using the EOPs of
the hyperspectral image, the reverse collinearity condition
can be used to form rays for each point of a 2D line. As the
image has no depth information, the lidar mesh is intersected
by the rays to give points in object space, thus transforming
the classification boundaries into 3D polylines and polygons.
This 3D information can be used to extract real-world
dimensions, allowing investigation of spatial relationships of
material distribution.
5.2 Point cloud classification and modelling
The 3D classification boundaries obtained in Section 5.1 can
be combined with segmented point clouds (Section 4.1) to
create triangulated meshes for individual material bodies.
This allows accurate calculation of surface area for the
bodies, and estimation of volumes where appropriate 3D
exposure is present. The procedure operates on a single
material class at a time; i.e. a set of lines and a segmented
point cloud representing one class. Each line is processed in
turn, first checking whether it is the outer boundary of a
body, or whether it is an inner hole indicative of a different
material class. All points of the point cloud are projected to
image space using the point of view of the capturing image,
to test whether they are inside the polygon. Polygons with no
containing points are designated as holes. Still working in
image space, the points and lines are triangulated, using the
holes as constraints for triangle removal. Finally, the internal
vertices of the triangulated body are assigned their original
3D lidar positions, and the points on the line segments are
projected to 3D as in Section 5.1 (Fig. 6).

Figure 6. 3D material bodies created using triangulated
points and classification boundaries, and
superimposed on textured model (Garley
Canyon). Inset shows detail of points and lines.
5.3 Assignment of geometric properties to imagery
Using the TLS geometry allows hyperspectral image
information to be accessed in 3D in object space units. For
remote sensing processing it can be useful to have per-pixel
geometric information, such as for topographic correction
and spatial analysis of results. For each image pixel, a ray is
defined and intersected with the 3D mesh, as in Section 5.1.
For pixels with no 3D data, such as those representing sky or
background, a no-data value is assigned. The surface normal
at the intersection point can be used to calculate slope and
aspect values for each pixel (e.g. Fig. 7), which are useful for
topographic correction and geological analysis. In addition,
the range from the camera centre to the intersected points can
be used for calculating object sample distance, giving a quick
approximation for material class areas in the remote sensing
software environment.

Figure 7. Slope-encoded image that can be used as a
processing mask in remote sensing software.
White areas are outside of the 3D model. Garley
Canyon image used (Fig. 2).
5.4 Accuracy considerations
Multi-sensor fusion products rely on accurate co-registration
of the component techniques to be used appropriately. For
close range hyperspectral data, the image registration is
critical and should be inspected. Whilst statistical confidence
is a result of bundle adjustment, it is also important to
evaluate the accuracy in an integrated way. Photorealistic
models (Section 4.2) textured with conventional photos and a
hyperspectral product give a qualitative indication of
registration accuracy, as the user can blend between the
layers to see the proximity of conjugate areas in both data
types. Projection of the classification vectors to 3D allows
their position with respect to the photorealistic model to be
compared geometrically (Kurz et al., 2011).
This paper has outlined the potential of integrated TLS and
close range hyperspectral data for simultaneously analysing
the geometry and distribution of materials. Examples from an
application in geology have been used to illustrate the data
fusion products, though the methods have potential use in
many other disciplines. The two data types are highly
complementary, and should be used together both during
processing and for later analysis. Results are often visual, and
it may be a challenge to link all information in a single
environment for interpretation. Photorealistic modelling with
multiple textures allows the hyperspectral results to be
spatially related to higher resolution conventional photos,
and lidar geometry, in a single interactive viewing
framework. Linking 2D image data to 3D introduces
quantitative means for analysing classification results, and
allows co-registration accuracy to be inspected both
statistically and visually.
This work was supported in part by the Research Council of
Norway's Petromaks programme (grants 163264 and
176132). Statoil ASA is thanked for supporting fieldwork at