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vectors do not correspond to roads. Figure 2b shows junctions (threshold is 0.1) and road-segment extremities, represented 
by black squares. The white squares are junctions matched with extremities (/N,, = 13). We note that except for dead 
ends, there is only one extremity which is not matched (upper part of central curve), because there is no junction in the 
neighborhood of the extremity. If the nearest junction had been matched, the central segment would have shifted to the 
right, away from the road. Figure 2c shows the initialization after re-localizing road segments. Figure 2d presents the final 
position of the snakes (a; — 100 and 8 = 4). We note that the final snake positions are closer to the center of the lines than 
the original vectors. The mean displacement of road segment points is 2.76 pixels and the variance of this displacement 
is 4.09. We had a mean number of iterations of 31 per pixel in the snake step, for a total of 641 pixels treated. 
The results without junction detection (Klang's approach with the same parameters as in Figure 2) are presented in Figure 
3. The mean displacement of road segment points is 3.63 and the variance of this displacement is 4.95. The mean number 
of iterations was 34 per pixel. Comparing Figure 2f and 3b, we can see why the mean displacement in Klang's approach 
is higher than ours. Some snake sections are displaced further from the road. Our results shown an improvement in the 
road's localization over Klang's results. 
Now we will present the results concerning generation of hypothesis for new roads. Figure 4 presents the new-road 
hypothesis generation step for the image in Figure 2a. Figure 4a shows the line junctions near the known network (N — 7) 
and Figure 4b shows the final result (L — 10 and S = 7). The interested reader will find more results and a study of the 
parameters in (Auclair-Fortier et al., 1999). 
7 CONCLUSION 
We have presented our approach for automatically updating road maps from high-resolution aerial images. The updating 
of road database involves the addressing of two problems, i.e., the correction of existing road location and the addition 
of new roads. Since roads are long and thin elements in high-resolution images, the method is based on the detection of 
line. Existing road location is not precise, but can provide a good indication of where roads are located as opposed to 
other elements such as rivers or railways. For correction, we use a snake approach, with the existing location as the line 
initialization. To bring the initialization closer to the road, we re-localize road segments on the basis of line junctions 
from the image. We include a line junction detector proposed by (Deschénes and Ziou, 1999). We have presented some 
results obtained with an image and a database provided by Geomatics Canada. For the addition of new roads, we apply a 
line-following algorithm starting from image junctions which are in the existing network. We have shown that there is an 
advantage for using line junctions by comparing with Klang's approach. 
REFERENCES 
Auclair-Fortier, M.-E, Ziou, D., Armenakis, C. and Wang, S., 1999. Automatic Correction and Updating of Road 
Databases from High-Resolution Imagery. Technical Report 241, Département de mathématiques et d'informatique, 
Université de Sherbrooke. 
Auclair-Fortier, M.-E., Ziou, D., Armenakis, C. and Wang, S., 2000. Survey of Work on Road Extraction in Aerial and 
Satellite Images. Technical Report 247, Département de mathématiques et d'informatique, Université de Sherbrooke. 
Berger, M.-O., 1991. Les contours actifs : modélisation, comportement et convergence. Thése de Doctorat, Institut 
National Polytechnique de Lorraine. 
Deschénes, F. and Ziou, D., 1999. Detection of Line Junctions and Line Terminations Using Curvilinear Features. Tech- 
nical Report 235, Département de mathématiques et d'informatique, Université de Sherbrooke. 
Guindon, B., 1998. Application of Spatial Reasoning Methods to the Extraction of Roads from High Resolution Satellite 
Imagery. In: IGARSS. 
Heipke, C., Mayer, H., Wiedemann, C. and Jamet, O., 1997. Evaluation of Automatic Road Extraction. Proceedings of 
International Archives of Photogrammetry and Remote Sensing XXXII, pp. 47—56. 
Kass, M., Witkin, A. and Terzopoulos, D., 1988. Snakes : Active Contour Models. The International Journal of Computer 
Vision 1(4), pp. 321-331. 
Klang, D., 1998. Automatic Detection of Changes in Road Databases Using Satellite Imagery. In: Proceedings of 
International Archives of Photogrammetry and Remote Sensing, Vol. 32, pp. 293-298. 
Steger, C., 1998. An Unbiased Detector of Curvilinear Structures. IEEE Transactions on Pattern Analysis and Machine 
Intelligence 20(2), pp. 113-125. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 95