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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
registration procedure had been repeated after considering the
radial lens distortion. The new parameters of the transformation
function are presented in Table 2.
After considering the radial lens distortion, the mean normal
distance between the laser and transformed photogrammetric
line segments turned out to be 0.58 m, which is within the
expected accuracy range. A sharp drop in the standard
deviations of the transformation function parameters also took
place as can be seen when comparing Tables 1 and 2. The
overall improvement in the spatial discrepancies after
introducing the radial lens distortion verifies its existence.
Scale | 1.018032 |+0.000663
Xrum) |^ 70s £0.18
Yr(m)| 2.2 £0.13
Zy(m), -2427 £0.11
Q(9?) | 4.926549 | +0.034478
o (?) | 0.603525
K(°) | 0.214818
+0.092137
+0.029516
Table 2. 3D similarity parameters between laser and photo-
grammetry models after distortion compensation.
4. CONCLUSIONS AND RECOMMENDATIONS
Analyzing the previous results, a set of conclusions can be
extracted from this study, mainly; the efficiency of the
suggested registration procedure in identifying the systematic
discrepancies between the involved surfaces. After a closer look
at the discrepancies’ behaviour, it was possible to justify the
cause and take the necessary remedial measures to remove such
errors. Also, straight line features proved its suitability to
establish a common reference frame for the laser and
photogrammetric surfaces, a result that has been suggested by
prior research work. The involved datasets in the experimental
section illustrated the compatibility between laser and
photogrammetric surfaces. However, it is important to precisely
calibrate both systems to guarantee the absence of systematic
biases. In addition, the two surfaces must be relative to the same
reference frame as a prerequisite for any further integration
between the two datasets. For example optical imagery can be
rendered onto the laser data to provide a realistic 3D textured
model of the area of interest.
Further research is required to address the automatic extraction
of different types of primitives from the surfaces in question.
Developing an automatic matching strategy between laser-
derived and photogrammetric features is an interesting
extension. For example, Modified Iterated Hough Transform
(MIHT) can be used to simultaneously determine the
correspondence between conjugate primitives in overlapping
surfaces and the parameters involved in the registration
transformation function. The type of transformation function
will also be looked at. So far, 3D similarity transformation has
been assumed as the registration transformation function
relating overlapping surfaces. Future work will investigate the
discrepancy pattern for different errors and factors such as
GPS/INS/Laser spatial and rotational biases or biases in the IOP
of the involved cameras.
5. REFERENCES
Baltsavias, E., 1999. A comparison between photogrammetry
and laser scanning, /SPRS Journal of Photogrammetry &
Remote Sensing, 54(1):83—94.
Brown, L. G., 1992. À survey of image registration techniques,
ACM computing surveys, 24(4):325-376.
Csathó, B. M., K. Boyer, and S. Filin, 1999. Segmentation of
laser surfaces, International Archives of Photogrammetry and
Remote Sensing, 32(3W 14):73-80.
Ebner, H., and T. Ohlhof, 1994. Utilization of ground control
points for image orientation without point identification in
image space, /nternational Archives of Photogrammetry and
Remote Sensing, 30(3/1):206—21 1.
Filin, S., 2002. Surface clustering from airborne laser scanning
data, /nternational Archives of Photogrammetry and Remote
Sensing, 32(3A):119-124.
Habib, A., and T. Schenk, 1999, New approach for matching
surfaces from laser scanners and optical sensors, /nternational
Archives of Remote . Sensing,
32(3W14):55-61.
Photogrammetry and
Habib, A., Y. Lee, and M. Morgan, 2001. Surface matching and
change detection using the modified Hough transform for robust
parameter estimation, Photogrammetric Record | Journal,
17(98): 303-315.
Habib, A., Y. Lee, and M. Morgan, 2002. Bundle adjustment
with self-calibration using straight lines, Photogrammetric
Record Journal, 17(100): 635-650.
Kilian, J., N. Haala, and M. Englich, 1996. Capture and
evaluation of airborne laser scanner data, /rrernational Archives
of Photogrammetry and Remote Sensing, 31(B3):383-388.
Lee, L, and T. Schenk, 2001. 3D perceptual organization of
laser altimetry data, International Archives of Photogrammetry
and Remote Sensing, 34(3W4):57-65.
Maas, H.-G. 2002. Methods for measuring height and
planimetry discrepancies in airborne laserscanner data,
Photogrammetric Engineering and Remote Sensing, 68(9):933-
940.
Postolov, Y., A. Krupnik, and K. McIntosh, 1999, Registration
of airborne laser data to surfaces generated by photogrammetric
means, International Archives of Photogrammetry and Remote
Sensing, 32(3W 14):95-99.
d
Schenk, T., 1999. Determining transformation
between surfaces without identical points, Technical Report
Photogrammetry No. 15, Department of Civil and
Environmental Engineering and Geodetic Science, OSU, 22
parameters
pages.
Schenk, T., and B. Csathó, 2002. Fusion of LIDAR data and
aerial imagery for a more complete surface description,
International Archives of Photogrammetry and Remote Sensing,
34(3A):310-317.
6. ACKNOWLEDGEMENTS
The authors would like to thanks Mosaic Mapping Inc, for
supplying - the aerial and laser datasets on which the
experimental work was conducted.
