ne XXXIX-B3, 2012
ointing is accomplished
re image
1] o; [m] RA
9 0.242 1/1963
3 0.482 1/1601
0 0.200 1/2813
0 0.300 1/1578
0 0.500 1/699
H =820m in RA
ck points
(7)
2
Y Sg
n,
Z
Y, Z coordinates
Joints
(8)
te value, Z, is better than
because the vertical
e constrained by laser
oposed method. On the
tive calibration accuracy
00 in comparison to the
concluded that camera
cal.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
4.2 Object extraction
Object extraction procedures using experimental data are shown
in Chapter 3. It can be said that object shape extraction using
the conversion between object space and image space for
camera calibration was successful. Figure 12 shows 3D
modelling of the object extraction result. Blue line shape is
obtained by manual plotting, red dots are rough shape by laser,
and green line is object extraction result. Object extraction
procedures can create 3D models; however, it produces strange
shapes owing to mismatches.
Figure 12. 3D modelling
5. CONCLUSIONS
A camera calibration technique and object extraction
procedures were developed in this study in order to achieve 3D
modeling using ALS data and digital camera images.
It is confirmed that camera calibration using pseudo GCPs and
simultaneous adjustment shows GSI restrictions of less than
1/500 and more than 1/1000 in generating each scale map.
Therefore, it is concluded that simultaneous adjustment using
pseudo GCPs, distance conditions, and geometric constraint
conditions is practical because the simultaneous adjustments
perform interior and exterior orientations without any GCPs or
aerial triangulation.
The object extraction procedure was established using ALS data
and digital images. The normal vector map is a useful tool for
operator interpretation and rough object shape extraction.
Moreover, it was effective at extracting object shapes by image
processing using ALS data.
However, there are some issues requiring further investigation.
These problems include accuracy improvement and automatic
generation of pseudo GCPs for camera calibration and object
shape extraction.
References
Abdel-Aziz, Y. I., 1982. Accuracy of the normal case of close-
range photogrammetry. Photogrammetric Engineering Remote
Sensing, 48, 207-213.
Ackermann, F., Ebner, H., Klein, H., 1972, Combined block
adjustment of APR Data and Independent Photogrammetric
Models, The Canadian Surveyor, Vol. 26, pp.384-396.
Canny, J., 1986. A computational approach to edge detection,
IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-8, No. 6, pp.679-698.
57
Chen, L., Teo, T., Rau, J., Liu, J., Hsu, W., 2005. Building
Reconstruction from LiDAR Data and Aerial Imagery. In
Proceedings of IEEE International Geoscience and Remote
Sensing Symposium, Vol.4, pp.2846-2849.
Chikatsu, H., Kasugaya, N., Murai, S., 1988, An Adjustment of
Photogrammetry Combined with the Geodetic Data and GPS,
16th International Society for Photogrammetry and Remote
Sensing, Vol.26, pp.110-121.
Chikatsu, H., Odake, T. 2006, Ubiquitous Digital
Photogrammetry by Consumer Grade Digital Camera,
International Archives of the Photogrammetry, Remote Sensing
and Spatial Information Sciences, Vol. XXXVI, PART 5 (CD-
Rom), ISSN 1682-1750.
EL-Hakim S.F. and Faig W., 1981, A combined Adjustment of
Geodetic and Photogrammetric Observations, Photogrammetric
Engineering and Remote sensing, vol.47, No.1, pp.93-99.
Hartley, RI 1993. Camera calibration using line
correspondences. In Proc. DARPA Image Understanding
Workshop, pp. 361-366.
Hu, J., You, S., Neumann, U., Park, K., 2004. Building
Modeling from LiDAR and Aerial Imagery. In Proceedings of
ASPRS 2004 Annual Conference [CD-ROM].
Murai, S., Matsuoka, R. and Okuda, T., 1984, A Study on
Analytical Calibration for Non-Metric Cameras and Accuracy
of Three Dimensional Measurement, International Archives of
Photogrammetry, vol.25, issue 5, pp.570-579.
Nakano, K. and Chikatsu, H., 2011. Camera-Variant Calibration
and Sensor Modeling for Practical Photogrammetry in
Archeological Sites, Remote Sensing, vol. 3, issue 3, pp. 554-
569.
Otsu, N., 1980. An Automatic Threshold Selection Method
Based on Discriminant and Least Squares Criteria, The
transactions of the institute of Electronics and Communication
Engineers of Japan, IECE J63-D(4), pp.349-356.