Neither the goals nor the procedures of 3D mapping are clearly defined yet
Geo-referencing
Today geo-referencing from satellite
images is well understood and controlled.
It is the least problem we encounter in
3D topo-mapping. In previous projects
we have collected a lot of experiences in
geo-referencing. We have used SPOT-5,
ALOS/PRISM, Cartosat-1, IKONOS and
Quickbird images over different testfields
worldwide (Germany, Italy, Japan, South
Africa, Switzerland, Turkey, Vietnam).
We have developed the software SAT-PP
(Satellite Image Precision Processing),
which includes several strict models for
the most important sensors and also the
related Rational Polynomial Function (RPF)
approaches. With this software we have
obtained consistent results in the subpixel
domain, both for planimetry and height and
for all sensors, using few (2-5) GCPs only.
We could show that RPCs usually provide
good relative orientation, while the absolute
orientation has substantial systematic
errors. These kinds of errors depend on
the satellite/sensor. In the best case they
represent just a bias (shift in coordinates),
in other cases we diagnosed higher order
terms. In any case the distortions can
be removed with the concept of biascorrected
RPCs and the use of 1-3 GCPs.
DTM generation
DTM generation is a key issue in topomapping.
If produced in manual mode
this does not constitute a problem, it only
needs time – a lot of time. Therefore we
turn towards automated DSM generation
by image matching. Image matching - in
its essence - is still an unsolved problem.
With our software SAT-PP, which includes an advanced matching module, we obtain
height accuracies between 1 and 5 pixels
from high-resolution satellite images,
depending on the type of terrain, land
cover, image texture and image quality.
While RMS errors in such tests show good
results we must note that in all these cases
substantial blunders (10 times the RMSE
and more) still exist in the data. This is
not acceptable. This can only be solved
by substantial and time-consuming postediting
of the DSM or by efforts to better
understand the reasons for such blunders
in image matching, with the aim to get rid
of them. Therefore the avoidance and/or
detection of blunders in the automatically
generated DSM is a critical point for
future research and development.
The next problem we are faced with is the
reduction of the DSM, produced by the
image matcher, to the DTM, as represented
in the landscape model. Although
there are some attempts available to
automatically perform the reduction, the
results are not convincing, because these
algorithms are purely based on geometrical
considerations. What is needed however
is an image or point cloud interpretation
approach which lets us understand what
kind of object we are dealing with in the
reduction process at a particular location.
Object extraction
We have experience with automated
and semi-automated feature and
object extraction, primarily in 3D city
modelling and 3D road extraction. In
3D city modelling we use our semiautomated
procedure CyberCity
Modeler (CC-Modeler) for building
extraction. With some examples derived
from IKONOS and Quickbird images
we could show to what extent and at
which resolution these objects can be
modelled from satellite imagery.
In road extraction we have developed
“LSB-Snakes” (Least Squares B-Spline
Snakes), a semi-automated technique
which allows us to model roads in 3D.
In addition, the well-known technique
of monoplotting can be used for object
extraction (Fraser et al., 2008). This
procedure works usually well, but with limited accuracy, depending on
the quality of the underlying DTM.
In the following test the measurements
of the topographic features (buildings,
forests, streets, lakes, single trees
and contour lines) for the map scale
1:25,000 were done by an experienced
stereo operator of our group. For the
other special topographic features
we got support from an experienced
topographer from swisstopo.
Pilot mapping project Thun
A key issue in mapping is the
interpretability of images of a particular
resolution. Currently this topic is in the
center of our interest, because we believe
there are misconceptions on this issue.
We are conducting investigations to find
out which objects can be extracted under
which geometrical resolutions. Here we
present some preliminary results. For more
details please see Gruen and Wolff, 2008.
For a first test we selected the test area
of Thun, Switzerland. This is a fairly flat
urban zone which is composed of areas
with single family and apartment houses
with parks, an industrial area, a military
airport and forest areas. This area contains
many of the important features of a
topographic map. For the manual drawing
of contour lines we extended the area to
a hilly region, including forest and open
areas without any substantial buildings.
The aim of this investigation was to
analyze the possibilities to identify and
map buildings, roads and other individual
features for a 1:25,000 topographic map by
using high resolution satellite images data.
For such a mapping scale we assumed that
a GSD of 1m or even higher is required. In
conventional mapping and map updating
aerial images of scale 1:30,000 are used
(which corresponds to a GSD of about 0.5
m). For our test area Thun two IKONOS
panchromatic stereo images (December
2003, GSD 1m) were available.
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