ROADS AND BUILDINGS FROM LASER SCANNER DATA WITHIN

ROADS AND BUILDINGS FROM LASER SCANNER DATA WITHIN
ROADS AND BUILDINGS FROM LASER SCANNER DATA WITHIN A FOREST ENTERPRISE
1
Rieger, W.1, Kerschner, M.2, Reiter, T.2, Rottensteiner, F.2
Institute for Surveying, Remote Sensing and Land Information
University of Agricultural Sciences Vienna
2
Institute of Photogrammetry and Remote Sensing
Vienna University of Technology
Austria
[email protected]
Commission III, Working Group 2
KEY WORDS: Digital Terrain Models, Feature Extraction, Automatic Break Line Detection, Building Detection.
ABSTRACT
Laser scanning with its ability to penetrate vegetation and its extremely high point density allows for a completely new approach to
semi-automatically delineate man-made features (“objects”) in forested areas as a basis for the management of such data in a GIS. In this
paper, emphasis is laid on the detection of roads and buildings from laser scanning data. The basis of our analysis is the generation of a
DTM actually representing the earth surface (no tree tops, no building roofs). From a slope model of the terrain, break lines can be
detected by applying standard edge extraction techniques. However, the slope model is still too noisy to deliver “good” (long,
continuous) break lines. Thus, a pre-processing step making use of an edge-enhancing filter becomes necessary. From the results of
break line detection, a new, geomorphologically revised terrain model can be derived. The break lines contain the road edges which can
be interactively selected by the user. With respect to roads, the line extraction results can be improved using a snake algorithm. Building
candidate regions can be detected from the differences of surface models derived from the original “last-pulse” and “first-pulse” laser
data and the rectified ground model. The algorithm is based on a classification of elevation difference models followed by the
improvement of classification results by a despeckle filter, the main problem being the distinction of tree tops from buildings. In this
paper the algorithms involved for the solution of the above tasks are described and first test results are presented.
KURZFASSUNG
Durch die Möglichkeit, Vegetation zu durchdringen sowie durch die extrem hohe Punktdichte wird es mit Hilfe der Laserabtastung
möglich, völlig neue Methoden zur halbautomatischen Erfassung von Kunstbauten („Objekten“) in bewaldeten Gebieten als Grundlage
für die Verwaltung solcher Daten in einem GIS anzuwenden. In dieser Arbeit wird ein Schwerpunkt auf die Erfassung von Straßen und
Gebäuden aus Laserscannerdaten gelegt. Grundlage unserer Analyse ist die Erzeugung eines digitalen Geländemodelles, das die
tatsächliche Erdoberfläche repräsentiert (ohne Baumkronen und Dächer). Aus einem Modell der Geländeneigung können unter
Verwendung von Standardalgorithmen zur Kantenextraktion Geländekanten abgeleitet werden. Da die Neigungsmodelle zu verrauscht
sind, um „gute“ (lange, zusammenhängende) Geländekanten liefern zu können, wird ein Vorverarbeitungsschritt mit einem
kantenverstärkenden Filter nötig. Aus den Ergebnissen der Geländekantenextraktion kann ein geomorphologisch bereinigtes
Geländemodell abgeleitet werden. In den Geländekanten sind auch die Straßenränder enthalten, die interaktiv als solche selektiert
werden können. Weiters können in diesem Fall die Ergebnisse der Kantenextraktion durch Anwendung eines Snake-Algorithmus
verbessert werden. Kandidatengebiete für die Gebäudedetektion können aus den Differenzen der originalen „last-pulse-“ und „firstpulse-“ Daten und des rektifizierten Geländemodelles abgeleitet werden. Der dazu verwendete Algorithmus beruht auf einer
Klassifizierung von Differenzhöhenmodellen gefolgt von der Verbesserung der Klassifizierungsergebnisse mit einem Despecklefilter,
wobei das Hauptproblem in der Unterscheidung von weit ausladenden Bäumen und Gebäuden besteht. In der vorliegenden Arbeit
werden die Algorithmen zur Lösung der oben angesprochenen Aufgaben beschrieben sowie die Ergebnisse eines ersten empirischen
Tests vorgestellt.
1 INTRODUCTION
1.1 Motivation
The intensive utilisation of forests leads to fast changes in
cultivation. Especially the update of forest roads and clearcuts is
usually very time-consuming and inaccurate, since neither
photogrammetry nor GPS yield satisfying results in densely
forested areas. The growing usage of geographical information
systems (GIS) is another reason for the need of accurate coordinate determination. Laser scanning with its ability to
penetrate vegetation and its extremely high point density allows
for a completely new approach to semi-automatically delineate
these man-made features, subsequently called “objects”. For the
Figure 1: A hill shaded view of the terrain model.
Figure 2: Slope model with position of detailed windows.
time being it is still a costly technique which is not feasible for
large areas; but the technological development may soon bring
new systems which allow to fly higher, and to have greater point
density across track, so that it may be economically possible to
have frequent laser scanner flights for large areas.
in the hill slope, so that in the resulting DTM, the road sides must
appear as sharp breaks.
1.2 Test site and data
As test site the research forest of the Vienna University of
Agricultural Sciences was used. The forest is positioned
approximately 60 km south of Vienna in hilly terrain with
elevations ranging from 350 to 750 m above sea level. The
vegetation is typical for central Europe. More details can be
found in (Rieger, 1999).
A laser scanner flight was taken during winter time (leafless
period) with last reflected pulse recorded. This flight is necessary
in order to obtain a high quality ground model. From that model
roads can be delineated as is shown in section 2. The same flight
may be used to create digital surface models which include the
building surfaces. The difference between the surface and the
ground models is used to extract buildings in the way described in
section 3. Here, summer flights were used instead of the winter
flight since they were available for the test site. The results may
even be better if the surface model is derived from the winter
data.
First a digital terrain model with a grid width of 20 x 20 cm2 is
calculated using only the laser ground points by applying a robust
estimator with a skew error distribution function in the program
system SCOP (Pfeifer et al., 1999). Figure 1 shows a hill shaded
map of the terrain. The digital terrain model is used for the
creation of a digital slope model which provides the first
derivative of elevation. At each grid point, the elevation angle of
the surface normal (“steepness”) is given in that model. This
slope model is converted into a digital image, where the greylevels represent the steepness of the terrain (figure 2). As no
break lines have been considered yet in interpolating the DTM, a
rather smooth slope model is produced with wide transition zones
between flat areas and steep areas.
The images show two narrow roads in a mountainous, forested
area. Figure 3 draws two profiles perpendicular to the roads. Up
to four break lines may be detected. Beginning at the left side, the
first break is between the hillside and the bank of the road (only
in the right profile). At the two road sides the second and the third
break appear. The fourth break is at the end of the ditch. Not all
2 EXTRACTION OF ROAD BREAK LINES
Digital terrain models (DTM) generated from laser points can be
rather detailed due to a huge number of measured points. Despite,
break lines in the terrain appear smoothed, unless they have been
introduced into the DTM interpolation. Usually, these break lines
are digitised manually in a stereo plotter. In this work we tried to
extract forest roads in mountainous areas from the DTM in order
to introduce them in a new DTM interpolation. Such roads are cut
2
3
4
1
2
Figure 3: Profiles perpendicular to roads.
3
4
Figure 4: Two details from the slope model in figure 2.
Figure 6: Results of the biased sigma filter.
four break lines are significant along the entire road, in fact the
breaks at the road sides are usually stronger than the outer two
breaks. Figure 4 shows two details from figure 2 with the position
of the profiles of figure 3. The roads themselves are relatively flat
and appear as bright strips in the slope image. They are
surrounded by the road bank and indentation as dark strips.
measurement ml is the average value of all such pixels that have a
grey-level value smaller than the one of the central pixel.
Analogously, mh is the average value of such pixels having a
greater grey-level value. Depending on which one is closer to the
old grey-level value of the central pixel, either ml or mh is
selected to be the new grey-level value.
As break lines in the terrain model correspond to abrupt changes
in the surface normals, they can be detected by applying an edge
extraction algorithm to the first derivative of elevation.
Unfortunately, standard edge extraction algorithms deliver only
short segments of break lines (figure 5) or even fail.
This filter is quite powerful in smoothing while still preserving
and even enhancing image edges. Unfortunately it often
introduces artefacts. In our case we use the edge enhancing
property of this filter for sharpening widely blurred edges. We
choose a filter extent of 3 by 3 m2 (corresponding to 15 by 15
pixels). The σ-range of the filter is ignored so that all pixels in the
neighbourhood are used to calculate the two average values ml
and mh.
The effect of the filter can be seen in figure 6 for the images of
figure 4. The transition zones between flat and steep (between
bright and dark) can be removed by this filter which strictly
assigns either the bright or the dark value to each pixel. The
resulting image is optimally pre-processed for subsequent edge
extraction.
The danger in applying such a strong non-linear filter lies in a
geometric displacement of image edges. In our case we could not
find any evidence for this suspicion. The extracted break lines fit
exactly to the ones measured in the field (c.f. section 2.4).
Figure 5: Edges extracted from the slope model.
2.2 Automatic Feature Extraction
2.1 Edge Enhancement
For achieving satisfying results the slope image has to be
prepared by sharpening the edges. For this purpose an operator
based on the ideas of the biased sigma filter (Lee, 1983) can be
used. The biased sigma filter is an edge preserving and edge
enhancing smoothing filter. In the original concept it determines
the new grey-level value of a pixel (denoted as the central pixel)
by calculating two measurements ml and mh using some of the
neighbouring pixels. Those pixels in a square neighbourhood, that
have a grey-level value in a defined range around the grey-level
value of the central pixel (e.g. ± three times the standard
deviation σ of the image noise), take part in the calculation. The
We use an algorithm for simultaneous extraction of point and line
features based on the Förstner Operator (Fuchs, 1995). From the
first derivatives of the grey levels a measure W for local texture
strength and a measure Q for isotropy of texture can be computed.
The average squared norm of the grey level gradients in a small
(e.g. 5 x 5 pixels) neighbourhood can be used for W. By applying
thresholds Wmin and Qmin to W and Q, each pixel can be classified
as belonging either to a homogeneous region, to a point region or
to a region containing a line. As the classification result is
especially sensitive to the selection of the threshold Wmin for
texture strength, this threshold is selected in dependence on the
image contents. The selection of Qmin is less critical because Q is
bound between 0 and 1 (Mischke et al., 1997).
The results of classification have to be thinned out. Points are
found at the positions of relative maxima of texture strength in the
point regions. Line pixels are relative maxima of texture strength
in the direction of the gradient of the grey levels (Fuchs, 1995).
Neighbouring line pixels have to be connected to line pixel
streaks by an edge following algorithm. Finally, these streaks are
to be thinned out and approximated by polygons. Both for line
pixels and points, the co-ordinates are estimated with sub-pixel
accuracy. The algorithm was also tested in an engineering
surveying environment and gave promising results (Mischke et
al., 1997).
2.5 Results of Automatic Break Line Extraction
Figure 7 shows the resulting break lines. Compared with the lines
in figure 5 they seem smoothed and connected to longer
segments. This is the merit of the biased sigma filter. The heights
of the extracted break lines have to be derived from the original
laser data.
In the case of break line detection, we are only interested in
extracting lines. However, the sound statistical background of the
algorithm makes it quite applicable for our purposes. As in our
case the grey levels represent the elevation angle of the surface
normals, the first derivatives of the grey levels correspond to the
changing rate of the terrain steepness, i.e. to the curvature of the
terrain. The positions of maximum directed texture thus
correspond to regions of maximum terrain curvature, and the
thinned-out regions (the results of edge extraction) correspond to
break lines in the terrain model.
Figure 7: Edges extracted from the pre-processed slope model.
This approach for extraction of break lines contains a
simplification because we only use the elevation angle of the
surface normal as an input for edge extraction. Actually, the
elevation can be derived by both co-ordinate directions, and a
more sophisticated way of detecting edges has to make use of
both derivatives. For example, the input could be stored as a
digital image containing two bands, each band corresponding to
the first derivative of the terrain in one of the co-ordinate
directions. Geometrically, our simplification means that we can
not detect break lines between flat terrain regions with equal
steepness but different slope directions. Such break lines typically
appear at symmetrical ridges. As we are especially interested in
extracting roads and because with respect to roads there are no
symmetric ridges, our simplification does not influence the results
of edge extraction with respect to our goals.
2.4 Semi-Automatic Extraction by Snakes
Still, some of the detected lines are broken, and separated
segments appear. Snakes can be used for bridge gaps and deriving
longer segments (Kass et al., 1988). They are commonly used as
semi-automatic line extraction tool in digital images. It is the task
of an operator to provide an approximation of the edge to be
extracted by some seed points. Then the snakes try to detect the
exact edge location automatically by minimising an energy
functional. By this energy functional, a balance between internal
forces (enforcing a smooth shape of the curve) and image forces
(pulling them to salient image features such as edges) is reached.
Gaps in the image edges are bridged in a smooth way by
emphasising the internal terms of the energy functional. For the
specific task of extracting parallel road sides, an extension to the
snakes concept, the twin snakes approach can be used (Kerschner,
1998). This method is less sensitive to the approximation of the
position and shape and has some potential for full automation for
this task. First investigations show promising results.
For an accuracy analysis of the break lines extracted from the
preliminary DTM, we compared them with geodetically measured
break lines. Thereby we found out that the whole terrain model
had a systematic shift of 2 m in y-direction and 1.2 m in xdirection (figure 8, left). The reason was an insufficient georeferencing of the original laser points because of lack of suitable
control features. The extracted road sides compared to the
measured ones could be used to determine the shift. After
correcting the geo-reference of the DTM the extracted break lines
are very close to the manually measured road sides (figure 8,
right). The roads are found with the correct width while the banks
seem to be wider. The reason for this is that the edges of the roads
are much sharper defined than the edges of the banks. Even
during the terrestrial recording it was difficult to determine the
edges of the banks. The discrepancies lie in the range of 1-2 m,
which is smaller than the definition accuracy of these lines in
nature.
Figure 8: Comparison between terrestrially measured and
automatically detected road edges before and after correcting the
geo-reference.
In the end we derive a new DTM from both the laser point cloud
and the break lines, and a geomorphologically revised digital
terrain model can be obtained.
3 EXTRACTING BUILDINGS FROM LASER DATA
The method suggested here has been partly published in (Kraus
and Rieger, 1999). At first an accurate ground elevation model is
needed which was done according to (Pfeifer et al., 1999).
Secondly, a “ground biased” (referred to as “last pulse model”)
and a “crown biased” (referred to as “first pulse model”) grid
elevation models are created. These elevation models are derived
from the raw laser data by a moving maximum respectively
minimum filter. Both models show vegetation, yet the amount of
tree points is much less in the last pulse model.
The models are called last and first pulse models since they were
basically derived from summer last pulse and summer first pulse
laser data, respectively. However, since there is very little
difference between the two flights (Rieger et al., 1999), it is well
possible to derive the elevation models from only one flight, even
from the winter flight. Here, the two models were derived from
summer first and last pulse, respectively, and used as provided by
the company “TopoSys” which took the laser flights.
Figure 9: Digital orthophoto of part of the research forest.
The grid models were used instead of the raw laser data for
several reasons:

the approach is much easier and is based on standard
software;

there is a necessity to analyse the data in some
neighbourhood which is difficult to undertake with the huge
number of laser dots;

the position of the laser dots is completely arbitrary, and it is
difficult to find topological neighbours. In a grid there is a
clearly defined neighbourhood between points;

the raw laser data must be filtered towards ground
respectively surface which is done as a preprocessing step
through the creation of the grid models. Work on the raw
laser data would not easily allow to do so.
The laser elevation models show absolute elevations which are
not feasible for the extraction process. In order to obtain “object
heights” it is necessary to reduce the surface models by the
ground elevation models which is done by simple subtraction.
The resulting models show first pulse respectively last pulse
object heights.
The following grid models are needed for the extraction process:



first pulse – ground;
last pulse – ground;
first pulse – last pulse.
Figure 10: DEMs from laser data. Grid width 1 m. Left: first pulse – ground; Right: last pulse – ground.
Linear gray coding for heights: Black means a height value of 0 m, white a value of 40 m.
Figure 11: First pulse – last pulse model. Gray shading and grid
width as in figure 10.
Figure 12: Building mask.
These models are now used for a classification step. Figure 9
shows a digital orthophoto of the area with the buildings of the
research forest. Figure 10 shows difference models first-ground
and last-ground, Figure 11 shows the model first-last. Now all
areas that exhibit differences larger than one meter in the model
first-last are assumed to be vegetation points. The threshold value
of one meter was chosen because it corresponds to the grid width
of one meter; buildings in Austria usually have roofs tilted less
than 100%, thus the difference between first and last pulse
models should not exceed 1 m.
Areas with values lower than 1 m in the data set last-ground are
assumed to be ground points which normally cannot be
penetrated by the laser. The threshold of one meter here
corresponds to the medium hillslope and ground vegetation.
Those areas with values lower than 1 meter in the data set firstlast and higher than 1 m in the data set last-ground are assumed to
be building points. Figure 12 shows the resulting mask after
applying a 3x3 despeckle filter. This mask can further be
improved by expanding the building areas (black) to the area that
shows values larger than 1 m in the data set last-ground (dense
areas). The resulting mask is shown in Figure 13.
The usage of aerial or satellite imagery may be of great help in
distinguishing between vegetation an man-made objects. Yet, the
proposed algorithm shows good results and works fully
automatic. It could be used to automatically find those areas were
there may be buildings (or rocks or other solid off-terrain
features). Manual inspection could then allow to further
distinguish between different object types.
4 CONCLUSION
Roads can be well extracted from laser scanner data of
mountainous regions. For deriving complete road networks (e.g.
for a GIS) a semi-automatic approach is advantegeous. The most
Figure 13: Building mask enlarged with first pulse model.
significant road sides, which are extracted automatically, can be
used to produce a geo-morphologically corrected DTM. Further
investigations will concentrate on extending the concept for
extracting general break lines (where only the direction of the
slope changes abruptly).
For buildings, the candidate regions can be detected fully
automatically. Visual inspection is still necessary to distinguish
buildings from large isolated trees and in order to assign
additional attributes for classifying them in a GIS. In addition, the
grid points inside the candidate regions have to be matched to
geometric 3D-building models.
Our results have shown the high potential of extracting spatial
information from laser scanner data.
5 ACKNOWLEDGEMENTS
The research was funded by the Austrian Science Fund (FWF) in
the projects P12812-INF, P13725-MAT and the Austrian
Research Program S7004-MAT.
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