The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008
order to assess equivalence, the same dataset from the same
camera was calibrated three times, each time using a different
distortion model. If the distortion models are equivalent, the
resulting IOP should be equivalent. In order to compare the
three sets of IOP, the stability analysis software was used to
assess the IOP similarity.
Ax = K,(r 2 -R„)x + fC 2 (r 4 -R^)x + P,(r 2 + 2x 2 ) + 2P 2 xy-A,x +A 2 y
Ay = K,(r 2 -R 2 )y+K 2 (r 4 -Ro)y + P 2 (r 2 +2y 2 ) + 2P,xy +A,y (1)
Ax = x(K 0 + K,r 2 + K 2 r 4 + K 3 r 6 ) + (1 + P 3 r 2 )(P, (r 2 +2x 2 ) + 2P 2 xy)
Ay = y(K 0 + K,r 2 + K 2 r 4 + K 3 r 6 ) + (1 + P 3 r 2 )(2P,xy + P 2 (r 2 + 2y 2 )) ( 2 )
^ (R 0 + R,r +R 2 r + R,T + R„r 4 + R 5 r 5 + R 6 r 6 R 7 r 7 ) —
“ X (3)
+ (1 + P 3 r 2 + P 4 r 4 )(P, (r 2 + 2x 2 ) + 2P 2 xy)
^ (R u + R,r+R 2 r‘ + R 3 r 3 + R 4 r +R 5 r +R 6 r + R 7 r )_
r
+ (1 + P 3 r 2 + P 4 r 4 )(2P,xy + P 2 (r 2 + 2y 2 )
The results from this equivalence analysis are presented in
Table 6. The RMSE values are all well under the size of a pixel.
From these experiments, we can thus conclude that the tested
distortion models are equivalent.
Distortion models:
Camera 1
RMSE (mm)
Camera 2
RMSE (mm)
Krauss vs. SMAC
0.0012
0.0009
Krauss vs. PCI
0.0010
0.0011
SMAC vs. PCI
0.0017
0.0004
Table 6: Equivalence analysis
5.4 Photogrammetric Reconstruction
Sections 5.1 and 5.2 have dealt with the calibration and stability
of the investigated cameras. In this section, the achievable
accuracy that can be obtained using the investigated terrestrial
cameras is analyzed. To assess the accuracy, the point targets
on the calibration test field were surveyed to millimetre
accuracy, using a Total Station. Photogrammetric
reconstruction was then performed, using the IOP from
calibration session 1 for Camera 1, and the imagery data
collected from session 3. The point targets were extracted
automatically from the imagery, using the procedure outlined in
Section 2.1. An RMSE analysis was performed between the
surveyed and reconstructed points. The mean, standard
deviation, and RMSE results are tabulated in Table 7. From
these results, it can be concluded that the achievable accuracy
of these cameras has been determined to be within one
millimetre of the results obtained using a Total Station. In
addition, considering the average object space pixel size is 2.3
mm, the RMSE from the reconstructed object space compared
to the Total Station survey results are less than half a pixel size
in the object space.
Mean AX± o x (mm)
-0.38 ±0.87
Mean AY ±0Y (mm)
-0.38 ± 1.04
Mean AZ± <i z (mm)
-0.38 ±0.77
RMSE X (mm)
0.92
RMSEy (mm)
1.07
RMSE Z (mm)
0.83
RMSEmtai (mm)
1.64
Table 7: Mean, standard deviation, and RMSE for the comparison
between the reconstructed and surveyed coordinates
6. CONCLUSION
This paper has addressed several issues that are coming to
surface with the increase in adoption of amateur digital cameras
for photogrammetric mapping applications. In particular, the
method and quality of camera calibration, as well as long-term
stability has been investigated. First a low cost and efficient
calibration technique was outlined, in which a test field
composed of linear and point features was utilized. An
automated method for the extraction of the point and line
features was then summarized, after which several stability
analysis methods were presented. The stability analysis was
then used to evaluate the equivalency of different distortion
models. Finally, the achievable accuracy of the tested terrestrial
cameras was investigated. These procedures were performed on
two Prosilica GC1020 CCD cameras, and the experimental
results were presented in Section 5. Based on these results, it
was determined that the tested amateur digital cameras
remained stable over time, and provided an accuracy of one
millimetre. These results can be used to promote the use of
small format digital cameras as an attractive alternative for
convenient and inexpensive close-range applications, such as
deformation monitoring of building structures. Furthermore,
although the tests conducted in this work were performed on
small format digital cameras for close-range photogrammetric
applications, the outlined calibration and stability procedures
and tools are valid for use in analysis for aerial applications.
ACKNOWLEDGMENT
The authors would like to thank the GEOIDE Network of
Centres of Excellence of Canada (TDMASR37) and NSERC for
their partial funding of this research.
REFERENCES
British Columbia Base Mapping and Geomatic Services (2006).
Small and medium format digital camera specifications. Draft
Report, Ministry of Agricultural and Lands Integrated Land
Management Bureau, BC, Canada.
Habib, A., and Morgan, M (2003). Automatic calibration of
low-cost digital cameras. Optical Engineering, 42(4), 948-955.
Habib, A., Quackenbush, P., Lay, J., Wong, C., and Al-
Durgham, M. (2006a). Camera calibration and stability analysis
of medium-format digital cameras. Proceedings of SPIE -
Volume 6312, Applications of Digital Image Processing XXIX,
11 pages.
Habib, A., Pullivelli, A., Mitishita, E., Ghanma, M., and Kim
E.,(2006b). Stability analysis of low-cost digital cameras for
aerial mapping using different geo-referencing techniques.
Journal of Photogrammetric Record, 21(113):29-43.
Hough, P.V.C. (1962). Methods and Means for Recognizing
Complex Patterns, U.S. Patent 3,069,654.
Prosilica Inc. (2007). Prosilica GC1020 user manual, Feb 21.
1064