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©Quickbird Original Image Copyright 2002, Digital Globe)
Figure 8. A patch of the Quickbird image shows the result of
image-and-map registration. Left: the original images. Right:
the boundary lines of parcel are colored as yellow and the blue
color lines denote wall features.
The primitive result derived using the geometric-structure-
matching technique for automatic registration of a sub-scene of
Quickbird imagery and a cadastral map is as shown in Figure 7.
The four bright polygons represent cadastral parcels. A patch
of the satellite image over the test area is shown as in Fig. 8,
with the wall feature (blue lines) and the corresponding
boundary line (yellow lines). Further work on improving the
automatic algorithm and the relevant evaluation on the results
of the image-and-map registration is in progress.
5. CONCLUSIONS
The algorithm of the proposed GSM technique has been
validated using the Quickbird image and the corresponding
cadastral map. The boundary lines and polygons of cadastral
parcels are used as the elements of geometric structure in the
studied case. Automatic techniques have been developed to
match image features and the corresponding vector data. An
error model in the procedures of image-and-map matching has
been proposed. The error model is required to implement the
algorithm of image-and-map registration in order to achieve
high level of automation. The error model provides a threshold
for optimising the results of the proposed GSM technique. The
experimental results show that the magnitude of the error on
the order of 4.5m resulted from the image-and-map registration
algorithm is possible, and those errors are comparable with the
predicted ones (4.2m). It is possible to eliminate the
requirements of manual intervention for registering images and
maps, provided that accurate vector data and header data of
satellite images are available. Further work on improving the
automatic algorithm and the relevant evaluation on the results
of the image-and-map registration is in progress. Potential
applications of the proposed algorithm include providing
ground control for fully automatic photogrammetry and
updating data of spatial information systems.
ACKNOWLEDGEMENTS
The result presented in this paper is part of the work in the
project NSC 92-2211-E-014-006 sponsored by the National
Science Council, ROC. The satellite image presented in the
paper is sponsored under the project NSC 91-2211-E-014-
007. The authors are grateful to the Government of
Taoyuan County, Taiwan, ROC, for providing a digital map
of cadastral parcels. The authors are also in debt to
Professor C.-C. Chang and his group and Dr. S.-A. Chen for
giving help on GPS field work and computational
adjustment for the GPS observations.
REFERENCES
Baltsavias, E.P., 2004. Digital ortho-images — a powerful
tool for the extraction of spatial and geo-information. /SPRS
J. of Photogrammetry & Remote Sensing, 51, pp.63-77.
Chen, P.-H. and Dowman, I, 2000. Geocoding using
stereo-generated DEMs and automatically generated GCPs.
International Archives of Photogrammetry and Remote
Sensing, Amsterdam, Netherlands, Vol.33, Part Bl, pp. 38-
45.
Chen, P.-H. and Dowman, Ll, 2001. A weighted least
squares solution for space intersection of spaceborne stereo
SAR data. /EEE Trans. on Geo-Science and Remote
Sensing, GE-39(2), pp. 233-240.
Dowman, L, 1998. Automating Image Registration and
Absolute Orientation: — Solution and“ Problems.
Photogrammetric Record, 16, pp. 5-18.
Habib, A. and Kelley, D., 2001. Single-photo resection
using the modified Hough Transformation.
Photogrammetric Engineering & Remote Sensing, 67(8), pp.
909-914.
Heipke, C., Pakzad, K. and Straub, B.-M., 2000. Image
Analysis for GIS Data Acquisition. Photogrammetric
Record, 16(96), pp. 963-985.
Morgado, A. and Dowman, L, 1997. A procedure for
automatic absolute orientation using aerial photographs and
a map. ISPRS J. of Photogrammetry & Remote Sensing,
52(4), pp. 169-182.
Shapiro, L.G. and Haralick, R.M., 1981. Structure
description and inexact matching. /EEE Trans. on Pattern
Analysis and Machine Intelligence, 3, pp. 504-519.
Shapiro, L.G. and Stockman, G., 2001. Computer Vision.
Prentice Hall, Inc., 580 pages.
Sowmya, A. and Trinder, J., 2001. Modelling and
representation issues in automated feature extraction from
aerial and satellite images, ISPRS J. of Photogrammetry &
Remote Sensing, 55, pp. 34-47.
Wang Y., 1998. Principle and applications of structural
image matching. ISPRS Journal of Photogrammetry &
Remote Sensing, 53, pp. 154-165.