integration of laser-derived dsms and matched image edges for generating an accurate surface model
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Integration of laser-derived DSMs and matched image edges for
generating an accurate surface model
Kerry McIntosh, Amnon Krupnik*
Department of Civil Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel
Received 20 August 2000; accepted 21 December 2001
Abstract
Airborne laser altimetry is a highly efficient and accurate method of obtaining data for the determination of visible surface
topography. With minimal processing, the laser data can provide coordinates of points on the visible surface with high spatial
frequency and precision. Although this technology has benefits compared to photogrammetric techniques, there are limiting
factors due to the laser data having no structural and textural information. These limitations are significant in low-density laser
data and may be overcome by utilizing both laser altimetry and photogrammetrically derived data in the surface determination
process. The research described in this paper has been undertaken to accurately determine the visible surface in urban areas using
airborne laser scanner data and digital aerial images. Edges detected and matched in aerial images are used to refine the digital
surface model (DSM) produced from airborne laser scanner data. The laser data and the edge information are merged to exploit
the benefits of each dataset, facilitating the generation of an accurate surface model. This model provides a better representation
of surface discontinuities, especially building walls. The paper presents the algorithms developed and shows that the surface
accuracy is improved by 49% and 15% for the two tested areas, respectively. D 2002 Elsevier Science B.V. All rights reserved.
Keywords: Data integration; Airborne laser scanning; Edge matching; DSM; Surface reconstruction; Surface discontinuity modelling
1. Introduction
Digital surface models (DSMs) of urban areas are
becoming widely used in an increasing number of
applications, such as digital orthophoto production,
3D city modelling and 3D building reconstruction.
Laser scanning is recognized as an accurate data
source for DSM generation in urban areas (Haala et
al., 1997). The spatial resolution of the data is depend-
ent on several factors, such as flying height, flying
speed and scanner frequency (Lemmens et al., 1997).
Laser data provide accurate points with high spatial
frequency, however, breaklines are not explicitly
present in the data (Ackermann, 1999; Axelsson,
1999; Haala et al., 1997; Kraus and Pfeifer, 1998),
and therefore, the position of surface discontinuities
can only be estimated or calculated by methods such
as segmentation of the range data (Haala et al., 1997;
Schenk et al., 1999). To illustrate this point, Fig. 1
presents an elevation image generated from laser data
with 2 m grid spacing showing high-rise buildings.
The poor definition of the building walls (as noted
also by Vosselman, 1999) is apparent in the ‘ragged’
nature of the building edges.
The spatial density of the laser data affects the level
of inaccuracy that is introduced due to the lack of
0924-2716/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.
PII: S0924 -2716 (02 )00042 -4
* Corresponding author. Tel.: +972-4-829-2661; fax: +972-4-
823-4757.
E-mail address: krupnik@tx.technion.ac.il (A. Krupnik).
www.elsevier.com/locate/isprsjprs
ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 167–176
terrain information. A high spatial density of data may
well provide enough information to define the surface
to the required accuracy, however, if the spatial den-
sity is low, extra information will be required to
accurately define the surface. The method described
in this research was aimed especially at such low-
density laser data.
Digital photogrammetric methods of automatic sur-
face reconstruction have become widely used due to
the efficiency and cost effectiveness of the production
process, especially in open and flat areas, and when
using small and medium scale imagery (Krzystek and
Ackermann, 1995). However, most software packages
perform poorly in areas with abrupt height differences,
such as urban regions (Haala, 1999). The decline in the
performance of softcopy photogrammetric packages
can be caused by failures of the image matching
process (Axelsson, 1998). Such failures may be due
to factors like lack of texture in the images (Haala,
1994), poor image quality, shadows in the images,
occlusions, surface discontinuities (Haala et al., 1997)
and foreshortening. These problems occur especially
when using digital photogrammetry in urban areas and
result in inaccuracies in the DSM, which can be seen in
the smoothed discontinuities of the reconstructed sur-
face, as well many blunders close to them (Baltsavias,
1999; Haala, 1999; Toth and Grejner-Brzezinska,
1999).
One of the benefits of photogrammetry is that the
imagery contains more information than just the
position of pixels in the images. Grey value changes
in the images allow the identification and classifica-
tion of objects, such as buildings or vegetation, and
can be used to detect edges in the images, which often
indicate the location of surface discontinuities (Balt-
savias, 1999; Fradkin and Ethrog, 1997; Haala and
Anders, 1997). Furthermore, utilizing imagery in con-
junction with surface reconstruction enables the pro-
duction of orthophotos and 3D views of the terrain.
Multispectral imagery can also help in the classifica-
tion of laser data.
Investigations and observations comparing DSMs
produced from laser data and those derived from digital
photogrammetric methods have been made in several
research studies. In areas of the imagery lacking texture
or contrast, the image matching might not provide
accurate results whereas the accuracy of the laser is
not affected (Baltsavias, 1999; Kraus and Pfeifer,
1998). However, photogrammetric data have a higher
planimetric accuracy than laser data (Baltsavias, 1999).
The complementary nature of the two data sources
has been widely recognized and their combination has
been suggested by researchers since several years
(Fritsch and Kilian, 1994; Haala, 1994). This sugges-
tion has been reiterated recently (Ackermann, 1999;
Axelsson, 1999; Baltsavias, 1999; Csatho et al., 1999;
Fritsch, 1999; Haala, 1999; Haala and Anders, 1997;
Toth and Grejner-Brzezinska, 1999; Vosselman, 1999).
The approach of using imagery to provide edge infor-
mation has been highlighted by several researchers
(Ackermann, 1999; Axelsson, 1998; Csatho et al.,
1999; Haala and Anders, 1997), and is the main
concept of the approach undertaken in this research.
It is proposed to merge the accurate edge informa-
tion from the imagery with the laser scanner data,
providing a more accurate dataset than either of the
two separate data sources. A preprocessing step is
required to generate surfaces from each data source
and accurately register the surfaces to the same coor-
dinate system. The main component of the proposed
approach combines information from each data source
to obtain the optimal topographic surface. The photo-
grammetric data are utilized by extracting edges from
the stereo imagery, and processing the edges using
feature-based matching techniques. Then, a 3D recon-
struction of the matched edges is performed. These
edges are used to obtain accurate locations of the
surface discontinuities in the urban scene. The laser
data is classified in ground and building points with
aim to detect 3D lines which correspond to building
rooflines. These lines are used as breaklines when
Fig. 1. Laser height data shown as grey level image.
K. McIntosh, A. Krupnik / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 167–176168
merged with the laser data. A new surface in trian-
gulated irregular network (TIN) representation is
generated using the merged data and constrained trian-
gulation.
Testing of the approach has been undertaken using
an urban site covering Ocean City, MD, USA. Laser
data and aerial images, acquired on the same day by
NASA and NGS, respectively, are used. Experiments
have been performed to test and refine the algorithm.
This paper presents the datasets, the preprocessing
step, the data fusion approach and detailed test results.
2. The data fusion approach
2.1. Data preprocessing: surface registration
The surface registration is undertaken to determine
the transformation parameters between the laser and the
photogrammetrically derived surfaces. Theoretically,
the two datasets should refer to the same coordinate
system. However, the systematic errors inherent in the
laser data may introduce a misalignment between the
two surfaces, which must be eliminated before data
fusion may be performed accurately (Kraus and Pfeifer,
1998).
Ideally, a seven parameter conformal transforma-
tion should have been used for converting the laser
data to the coordinate system of the photogrammetric
model. However, this requires surfaces with different
surface normal directions in order to avoid singularity.
Here, only three shift parameters were used and they
were sufficient for accurate co-registration of the two
datasets. The determined transformation parameters
must be as accurate as possible to ensure that no
unnecessary degradation occurs in the accuracy of the
surface generated from merging the datasets.
The surface registration algorithm was developed
specifically for matching visible surface models of
urban areas produced using different acquisition meth-
ods, and in particular, surfaces from airborne laser
scanner data and surfaces automatically generated
using photogrammetric data. Both the laser surface
and the photogrammetrically derived surface are rep-
resented by irregularly distributed elevation points.
Defining the laser elevation points as the source surface
that should be transformed into the other (target) sur-
face, an observation equation can be written for each
surface point. The actual observation is the elevation
difference between the two surfaces at the planimetric
location of each laser point. Since the elevation of the
target surface at this location is generally not known, a
mathematical surface is fitted to the photogrammetri-
cally derived points in the vicinity of the laser data
point. This surface provides both the elevation of the
target surface at the required location, and the surface
gradients, which are necessary for the linearized obser-
vation equations. A detailed description of the surface
matching algorithm is provided in Schenk et al. (2000).
Another approach for matching such surfaces is given
by Habib and Schenk (1999).
2.2. Edge extraction
The edge pixels are detected in each image of the
stereo pair. At present, the optimal zero-crossing
operator (Sarkar and Boyer, 1991) is used for the
detection of edge pixels. The edge pixels are analyzed
to find connected edge chains, and using these chains,
straight line-segments (termed edges in the following)
are determined. Straight edges are utilized as building
rooflines as they are more accurately located than
point features when using feature matching techniques
(Fradkin and Ethrog, 1997).
The automatic extraction of edges has certain
limitations that must be addressed. Using automatic
extraction methods, not all surface discontinuities are
detected due to factors such as lack of contrast in the
imagery. There may be a number of incomplete or
incorrect edges, and edges that do not represent sur-
face discontinuities (breaklines) may be detected.
These particular breaklines may refer to visual edges
in the images that are purely changes in gray value,
such as road marks. Including these edges as break-
lines in the DSM is not expected to be detrimental to
the data fusion process.
2.3. Edge matching
The edge matching approach currently being used
for this research is based on that of Schmid and
Zisserman (1997), and employs the fundamental
matrix (F matrix) to guide the matching process. Edges
arematched by averaging the correlation coefficients of
conjugate points along them. Only edges longer than
10 pixels were used, as shorter edges are more likely to
K. McIntosh, A. Krupnik / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 167–176 169
represent noise rather than actual features. Given an
edge in one image, the epipolar lines that correspond to
its endpoints in the other image are determined by the
fundamental matrix. Knowing the elevation range of
features on the ground (which translates to a disparity
range), a parallelogram is drawn (Fig. 2, top right),
which constitutes the search space for edge segments in
the second image (Medioni and Nevatia, 1985). Can-
didates for matching will be portions of edges that fall
into this search space, as shown in Fig. 2 (top right),
with the limitation that the edges in the two images
should have a similar direction (e.g., edge C in Fig. 2
top right in not accepted as a candidate). This constraint
is justified when a normal aerial model is used, i.e., the
images are nearly vertical and their x axes are almost
parallel. More than one edge in the second image can
correspond to an edge in the first image, providing that
they do not overlap in the epipolar line direction. Thus,
the pair of edges marked ‘‘A’’ in the right image in Fig.
2 (middle right) may correspond to two portions of the
edge in the left image, while the two edges marked ‘‘B’’
are not accepted as possible match candidates because
they overlap. Selecting candidates for matching is
performed in both directions, i.e., selecting an edge in
one image and defining a search space in the second
image, and vice versa.
Cross-correlation coefficients are calculated be-
tween each pixel of the edge in the first image and its
corresponding pixel on the candidate edge in the other
image. The nearest pixel to the intersection between the
epipolar line defined by each edge pixel of the first
image and the candidate edge is selected (Fig. 2, bottom
right). The correlation window size was set to 15� 15
pixels, as suggested by Schmid and Zisserman (1997).
The measure for selecting the best match is the max-
imum of the average correlation value along the edge.
The calculation of the cross-correlation takes place
only on the common overlapping part of the two edges,
i.e., the portion of the candidate edge in the second
image that falls within the search area.
It should be noted that the approach taken here for
edge matching is not optimal. Thus, for example,
edges along the epipolar lines cannot be matched.
Other edge matching techniques may be used at this
point, and further investigation will be undertaken to
determine the optimal technique.
2.4. 3D segment reconstruction
The 3D coordinates of the edges are calculated using
the interior and exterior orientation parameters of the
aerial imagery. The end points of the overlapping
section of the edges are required in the image coordi-
nate system for each image. As the edge information is
in pixel coordinates, an affine transformation is used to
convert the information appropriately. The interior
orientation of the imagery defines this affine trans-
formation, and the rotation matrix and camera position
are given by the known exterior orientation. The 3D
coordinates of each endpoint of the edges are calculated
using this information and least-squares forward inter-
section (see Kraus, 1993, p. 15 for equations). In order
to avoid blunders, the 3D coordinates of the segment
end points are compared to the laser data elevation,
interpolated at the same planimetric location. Only
Fig. 2. Edge matching procedure: definition of the search space—
relevant candidates are marked with dots at the ends (top); matching
an edge in one image to two edges in the other image (middle);
definition of correlation windows (bottom).
K. McIntosh, A. Krupnik / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 167–176170
segments with end points that are within a 1-m eleva-
tion range from the interpolated elevation are consid-
ered for further processing.
One should note that the reconstruction procedure
generates breaklines, as well as 3D lines which are the
result of intensity discontinuity only. Although the
latter are not necessary for surface reconstruction,
including them in the process hardly makes any
difference. Therefore, no attempt was made to differ-
entiate between them and real breaklines.
2.5. Surface discontinuities
In an urban area, most surface discontinuities are
associated with man-made objects in particular build-
ings. These discontinuities are used as constraints
during the triangulation process, as described in Sec-
tion 2.6. The surface is well described if both the roof
outlines and the building outlines at ground elevation
are available. Most of the former are included in the set
of 3D segments obtained during the reconstruction
step. However, using imagery cannot provide a com-
plete set of the breaklines at ground level.
In order to obtain as many breaklines as possible, the
following approach was used. In the first step, a
classification of the laser points was performed. An
ideal classification would differentiate points on top of
buildings from points on top of vegetation and points
on the ground. Here, only a rudimentary classification
was utilized, which separated building points (defined
as higher than h + 0.75r, where h is the mean and r is
the standard deviation of the laser point elevations) and
ground points. Themean and standard deviation should
be calculated for a sufficiently small neighbourhood of
each point (here, the whole test areas were used). The
threshold was obtained empirically, and is believed to
be suitable for urban areas with flat terrain, and one to
two storey buildings. Using the classification results,
he laser points in the vicinity of each 3D segment
(a buffer that contains 5–10 points at each side of the
segment) were analysed. A 3D segment was charac-
terized as a breakline if most laser points on one side
were classified as ground points, while most points on
the other side were classified as building points.
Further analysis characterized breaklines as rooftop
or ground breaklines. This was done by comparing the
average elevation of the 3D breakline with the neigh-
bouring ground and rooftop laser points.
In order to obtain additional ground breaklines, each
rooftop breakline was paired with a ground breakline
wherever available. This was done by searching for
ground breaklines which were parallel and within 0.5m
distance (in planimetry) from a rooftop breakline. For
rooftop breaklines that did not have a corresponding
ground breakline, an additional ground breakline was
added as follows. The rooftop breakline was projected
vertically onto the ground surface to produce a new,
ground breakline. The elevation values along the pro-
jected breakline were determined by interpolating a
bilinear surface, using the ground laser points. The
result is a representation of the vertical wall surface by
two 3D lines, one at roof height and one at ground level.
Since the implementation used for triangulation (as
well as most similar implementations) does not allow
multiple elevation values at the same planimetric
location, the bottom line was shifted slightly outwards
Fig. 3. Images of the test areas A (top) and B (bottom).
K. McIntosh, A. Krupnik / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 167–176 171
parallel to itself. As this shift is very small (it only needs
to introduce a numerical difference between the two
planimetric locations), the slope introduced in the
resulting surface is insignificant.
2.6. TIN generation
The input for the TIN generation process com-
prises of the laser data points and the breaklines. Laser
points that are located close to the breaklines are
eliminated in order to avoid inaccurate responses from
the wall itself, or when a laser beam falls exactly on
the edge of a roof. The breaklines (represented as a set
of straight 3D segments) are introduced as triangle
legs, and constrain the triangulation process in a way
that no new triangle leg can cross them. The inclusion
of the elevation information at the TIN nodes allows
the production of the digital surface model using the
combined laser data and photogrammetrically derived
3D segments.
3. Tests and results
Testing was carried out using a dataset over Ocean
City, MD, USA, which includes digital stereo imagery
Fig. 4. Test area A—extracted and matched line segments.
K. McIntosh, A. Krupnik / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 167–176172
and laser data. The B/W images were taken by a Wild
RC 10 wide-angle camera, at a scale of 1:3500 and a
forward overlap of 75%, and scanned by a photo-
grammetric scanner at 28 Am. The laser data was pro-
duced by NASA’s ATM conical scanner, with an
average ground spacing of 2 m, and a footprint of
1.5 m. Only one response of the laser pulse was re-
corded (see Csatho et al., 1998 for more details). The
dataset covers different types of areas, including
residential areas, flat terrain, beach front, dunes,
canals and high-rise buildings. Two sections of the
residential area have been used for the testing of the
algorithm. Fig. 3 shows images of these sections.
3.1. Data processing
The data were processed with the methods ex-
plained in Section 2. The transformation parameters
for the co-registration of the two datasets were deter-
mined as follows. Rooflines were measured manually
at a digital stereoplotter to produce planes. These
planes were used with the laser points in the registration
Fig. 5. Test area A: surface derived from laser points (top); surface
derived from laser data and manually measured breaklines (middle);
surface derived from laser data and automatically generated break-
lines and added ground breaklines (bottom).
Fig. 6. Test area B: surface derived from laser points (top); surface
derived from laser data and manually measured breaklines (middle);
surface derived from laser data and automatically generated break-
lines with added ground breaklines (bottom).
K. McIntosh, A. Krupnik / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 167–176 173
process described in Section 2.1 for calculating the
shifts in X, Y and Z directions. Once these were cal-
culated, the laser surface was transformed into the
coordinate system of the roof planes. Measuring the
roofs manually allowed a good registration, which is
independent of the 3D segment generation technique.
3D lines were generated for both test areas using the
procedure described in Sections 2.2–2.5. Fig. 4 (top)
shows the edges extracted from the left and right ima-
ges of test area A. The matched 3D segments, projected
on the left image are shown in Fig. 4 (bottom).
In order to evaluate the quality of the edge matching
procedure, breaklines were also measured manually. In
addition, ‘‘ground truth’’ measurements were made
over the two test areas to determine quantitatively the
improvement in the surface representation when using
the merged data. Dense grids (0.5 m spacing) were
measured over the entire surface (ground and roofs)
covered by the two areas. The breaklines and the grids
Table 1
Comparison of ground truth data to surfaces from laser data and
merged data
Ground truth Compared to RMS (m)
Area A Laser 1.14
Laser + reconstructed breaklines 0.59
Laser +manually measured breaklines 0.52
Area B Laser 0.72
Laser + reconstructed breaklines 0.61
Laser +manually measured breaklines 0.53
Fig. 7. Histogram of height differences (in m) between ground truth and: laser data (top), laser data and reconstructed breaklines (bottom).
Results shown for test area A (left) and test area B (right).
K. McIntosh, A. Krupnik / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 167–176174
were measured at a softcopy photogrammetric work-
station, and their accuracy is estimated to be 5 cm in
planimetry and 10 cm in height.
3.2. Results
Three types of surfaces were generated for the test
areas. The first surface was based only on the laser
points, and serves for comparing the results of the
approach presented here to a standard laser-based
DSM. The second surface was generated using the
laser data and the manually measured breaklines. This
is the representation of the visible surface that the
research attempts to achieve automatically. The third
surface is generated using the laser data and the
automatically extracted breaklines.
Fig. 5 shows the three surfaces for test area A. A
visual comparison of the top and bottom surfaces
shows the significant improvement achieved by includ-
ing the automatically derived breaklines with the laser
data when determining the visible surface model over
an urban area. The middle surface, which utilized the
manually measured breaklines, is only slightly better
than the bottom surface. The results of test area B are
shown in Fig. 6. Here, again, the improvement in the
representation of the surface is apparent when compar-
ing the top and bottom surfaces.
A quantitative analysis of the results was per-
formed by comparing the surfaces to the manually
measured grids points. These grid points were used as
check points for the surface created from the laser
data, the surface created from the laser data that were
merged with the automatically reconstructed break-
lines, and the surface created from the laser data
merged with the manually measured breaklines. The
RMS of the height differences for both areas are given
in Table 1.
The comparison of the datasets described in Table 1
has also been analyzed and depicted as histograms of
height differences. Fig. 7 shows the comparison of the
ground truth surface for test areas A and B to the laser
points (top) and the surface generated using the laser
points and reconstructed breaklines (bottom). The
reason for the major improvement when using break-
lines, observed when comparing the two left histo-
grams is rooted in the laser data themselves. The
number of the laser points that fall on the roof of the
right (dark) building in test area A is very small,
possibly due to poor reflection of the laser beam at
dark surfaces. Therefore, when reconstructing the sur-
face only with laser points, the outline of this building
is considerably smoothed, as can be clearly seen in Fig.
5 (top). Including the breaklines significantly improves
the building shape.
4. Conclusions
This paper describes the development of an algo-
rithm for the integration of laser height data and
photogrammetrically derived 3D segments. Tests of
the algorithm have been carried out using data over
Ocean City, MD, USA. Obviously, obtaining an
accurate and complete surface model automatically
for this urban area by utilizing images with only
pairwise overlap would be impossible with the current
technology. The tests have shown that the visible
surface is represented more accurately when using
the combined data than when using the laser data
alone. The improvement is noticeable both by com-
paring the reconstructed surfaces visually, and by a
quantitative comparison using manual ground truth
measurements. In the two test areas, the use of the
combined data led to an accuracy improvement of
49% (55 cm) and 15% (11 cm), respectively.
Further research is required with respect to several
aspects of the suggested algorithm. The edge match-
ing and the laser data classification used in the current
implementation are not optimal. It is expected that
improvement of these parts, especially a proper sep-
aration of buildings from vegetation and the ground
surface, will improve the selection of relevant 3D
segments for constraining the surface. Additional
investigation is also required for adapting the algo-
rithm to non-flat terrain. It should be also noted that
the laser data used here is rather sparse. Therefore, it
would be also interesting to check the effect of adding
breaklines to denser laser data.
Acknowledgements
This research project is supported by the United
States–Israel Binational Science Foundation, Grant
No. 97-00433, and by the Fund for the Promotion of
Research at the Technion.
K. McIntosh, A. Krupnik / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 167–176 175
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