ISPRS Commission III, Vol.34, Part 3A „Photogrammetric Computer Vision“, Graz, 2002 
EOP and for obtaining the correspondence between the two data 
sets in addition to reliably highlighting the changes. 
a. 
= 
LAN 
    
    
fe Original Points 
4X Matched Points 
Non-Matched Points 
  
  
Figure 4. Example of detected changes between road segments 
in image and object space. 
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5. CONCLUSIONS AND RECOMMENDATIONS FOR 
FUTURE WORK 
The MIHT for robust parameter estimation technique has been 
used to perform SPR for real data using free-form control linear 
features without knowing the correspondence between image 
and object space primitives. The proposed technique robustly 
estimates the parameters. In other words, the parameters are 
estimated using common features in both data sets (object and 
image space features); while non-corresponding entities are 
filtered out prior to the parameter estimation. An optimum 
sequence for parameter estimation and the associated image 
regions had been established and implemented. The proposed 
method has successfully established the feature-to-feature 
correspondence between the image and object space. It has also 
highlighted discrepancies (changes) between the object and 
image space road network and provided a quantitative measure 
indicating the amount of the change. The proposed system has 
the capability of integrating aerial imagery with GIS data or 
terrestrial mobile mapping system for decision-making purposes 
(e.g. re-mapping of road network). In this way, newly acquired 
aerial imagery can undergo SPR using available control 
information from a terrestrial mobile mapping system, previous 
imagery, GIS database or line maps. Currently, we are analysing 
the optimum pixel size of the accumulator array corresponding 
to different parameters at various iterations. In addition, 
generating rectified ortho-images using matched control linear 
features will be investigated in future research. 
Acknowledgement 
The authors would like to thank Ms. Young-Ran Lee, Mr. 
Hsiang Tseng Lin and Mr. Suyoung Seo for their help in 
collecting the data used in the experiment section. Also, we 
thank other members in the OSU photogrammetry group for the 
helpful discussions and feedback. 
6. REFERENCES 
Drewniok, C., and Rohr, K., 1997. Exterior Orientation-An 
Automatic Approach Based on Fitting Analytic Landmark 
Models. ISPRS Journal of Photogrammetry and Remote 
Sensing, 52(1997): 132-145. 
Fischler, M., and Bolles, R., 1981. Random Sample Consensus: 
A Paradigm for Model Fitting with Applications to Image 
Analysis and Automated Cartography. Communications of the 
ACM 24, 6, pp.381-395. 
Gülch, E., 1994. Using Feature Extraction to Prepare the 
Automated Measurement of Control Points in Digital Aerial 
Triangulation. International Archives of Photogrammetry and 
remote Sensing, Vol. 30, Part 3/1, pp..333-340. 
Haala, N., and Vosselman, G., 1992. Recognition of Road and 
River Patterns by Relational Matching. International Archives 
of Photogrammetry and remote Sensing, Washington, D.C., 
Vol. 29, Part B, pp.969-975. 
Habib, A., 1999. Aerial Triangulation using Point and Linear 
Features. International Archives of Photogrammetry and 
remote Sensing, München, Germany, Vol. 32, Part 3-2W5, 
pp.137-141. 
Habib, A. and Novak, K., 1994. GPS Controlled Triangulation 
of Single Flight Lines. International Archives of 
Photogrammetry and remote Sensing, Vol. 30, Part 2, pp.203- 
209. 
Habib, A., Asmamaw, A., and Kelley, D., 2000a. New 
Approach to Solving Matching Problems in Photogrammetry. 
XIXth ISPRS Congress, Amsterdam, The Netherlands, Vol. 33, 
Part B2, pp.257-264. 
Habib, A., Asmamaw, A., Kelley, D., and May, M., 2000b. 
Linear Features in Photogrammetry. Report No. 450, 
Department of Civil and Environmental Engineering and 
Geodetic Science, The Ohio State University, Columbus, Ohio, 
USA, 80p. 
Habib, A., Morgan, M., and Lee, Y., 2001. Integrating Data 
from Terrestrial Mobile Mapping Systems and Aerial Imagery 
for Change Detection Purposes. The 3™ International 
Symposium on Mobile Mapping Technology, Cairo, Egypt. 
Hough, 1962. Methods and Means for Recognizing Complex 
Patterns, U.S. Patent 3,069,654. 
Leavers, V., 1993. Which Hough transformation? CVGIP 
Image Understanding, 41(2): 250-264. 
Mikhail, E., 1993. Linear Features for Photogrammetric 
Restitution and Object Completion. Integrating 
Photogrammetric Techniques with Scene Analysis and Machine 
Vision, SPIE proc., Orlando, Florida, USA, No. 1944, pp.16- 
30. 
Mikhail, E., Akey, M., and Mitchell, O., 1984. Detection and 
Sub-pixel Location of Photogrammetric Targets in Digital 
Images. Photogrammetria, 39(1984): 63-83. 
Mulawa, D. and Mikhail, E., 1988. Photogrammetric Treatment 
of Linear Features. International Archives of Photogrammetry 
and remote Sensing, Vol. 27, Part B3, pp.383-393. 
Slama, C., 1980. Manual of Photogrammetry, 4th edition, 
American society of photogrammetry, pp. 574-577. 
Zalmanson, G., 2000. Hierarchical Recovery of Exterior 
Orientation from Parametric and Natural 3-D Scenes. Ph. D. 
Thesis, Department of Civil and environmental Engineering and 
Geodetic Science, The Ohio State University, Columbus, Ohio, 
USA, 129p. 
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