features in each sub-
| are merged together
ar features along the
nts of linear features
ndary, see red points
of upper and lower
eration. In order to
poundary, all linear
al direction based on
step is performed in
> bridge is rotated to
boundaries can be
rdinate. For straight
tion is used to fitting
boundary, 2" order
polynomial function
undary can be then
| computed via the
ire 10.
i
puted via 2° order
the Hough linear
|
R intensity and aerial
can be transformed
ity image as well as
nd lower boundaries
oarse boundary from
smooth boundaries,
| between the smooth
blue) extracted from
a precise DBM. In
1 boundary points are
our points’ elevation
ith the DTM derived
7.588
7.5875
1.8545 1.8546 1.8547 1.8548 1.8549 1.855
L
1.8548 1.8549 1.855
1.8545 1.8546 1.8547
x10
(b)
Figure 11. Smooth boundary points computed via 2" order
polynomial function (blue) according to the Hough linear
feature endpoints (red)
4. RESULTS AND CONCULUSION
A comparison between bridge ROI DSM derived from LiDAR
data and DSM based on the precise DBM is given in Figure 12
and Figure 13 for the test bridge 1and 2, respectively.
(b)
Figure 12. Bridge ROI DSM derived from LiDAR data (a)
and DSM based on the precise DBM (b) (bridge 1)
33
(b)
Figure 13. Bridge ROI DSM derived from LiDAR data (a) and
DSM based on the precise DBM (b) (bridge 2)
Clearly, the bridge ROI DSM is refined in both cases; the
surface and outlines of the bridges are smoother. For the test
bridge 2, the “collapsed” bridge section due to tree crowns is
well repaired. DSM with precise DBM can be used to improve
the orthophoto generation of the highway corridor areas. In this
paper, a new precise DBM generation method based on fusing
LiDAR and aerial image data is introduced. The novel idea is to
transfer smooth bridge boundaries extracted from the aerial
image to the LiDAR data via the established co-registration
between LiDAR intensity and aerial images. Note only
approximate exterior orientation of the aerial image should be
known. In case aerial imagery is not available, other high
resolution satellite image can be also used. In this paper, the
method is applied to produce precise DBM of straight and
curved bridges by fusing LiDAR data and aerial images. In
future research, complex multiple-layer bridge models using
this methodology will be investigated. In addition, the
possibility to apply this method to generate digital building
model of complex building structures will be also explored.
REFERENCE
Comaniciu, D., Ramesh, V., Meer, P., 2003. Kernel-based
object tracking. IEEE Transcations on Pattern Analysis and
Machine Intelligence, Vol. 25, Issue 5, pp. 546-577
Fischler, M. A., Bolles, R. C., 1981. Random sample consensus:
a paradigm for model fitting with applications to image analysis
and automated catography. Communications of ACM, Vol. 24,
pp. 381-395
Goepfert, J., Rottensteiner, F., 2010. Using building and bridge
information for adapting roads to ALS data by means of
network snakes. International Archives of Photogrammetry,
