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that have different resolution. Six classes were defined; and for 
each class training data were collected and images were 
classified. Then classified images were reclassed as two classes; 
road and nonroad by merging classes other than road and results 
were compared. Although the classified data of high resolution 
images produced better results, several problems experienced; 
When the pavement of road surface changes (e.g. from asphalt 
to concrete) the contrast changes so the same road could be 
assigned different cluster label. 
Due to very high resolution, vehicle, overpass, and other objects 
on a road could cause misclassification for that particular part. 
Other objects such as house roofs, which have same spectral 
property as roads, are misclassified into road clusters. 
Figure 4: Classified images with two classes. 
3. CONCLUSION 
• Many objects which can be recognized from high 
resolution images can not be detected distinctly in a low 
resolution image. 
• In a low resolution image roads can be extracted by 
using basic methods but the results have low accuracy. 
• In a high resolution image many detail information 
about roads can be obtained and the roads can be 
detected more accurately. However complex methods 
are required for this process. 
• Finally, the accuracy of road extraction from high 
resolution images is more than that one from low 
resolution images. 
REFERENCES 
Baltsavias, E., Gruen, A., van Gool, L. (Eds.), 2001. 
“Automatic Extraction of Man-Made Objects from Aerial and 
Space Images (III). Balkema, Lisse, The Netherlands”. 
Fortier, M.F., Ziou, D., Armenakis, C. and Wang, S., 1999. 
“Survey of Work on Road Extraction in Aerial and Satellite 
Images”. Center for Topographic Information Geomatics, 
Ontario, Canada. Technical Report No. 241. 
Gruen, A., and H. Li, 1997. “Semiautomatic linear feature 
extraction by dynamic programming and LSB-snakes”, 
Photogrammetric Engineering & Remote Sensing, 63(8):985- 
995. 
Gruen, A., Baltsavias, E., Henricsson, O. (Eds.), 1997. 
“Automatic Extraction of Man-Made Objects from Aerial and 
Space Images (II)”. Birkha'user Verlag, Basel. 
Koike, K., Nagano, S. And Ohmi, M., 1995, “Lineament 
Analysis of Satellite Images Using A Segment Tracing 
Algorithm (STA)”, Computers and Geosciences, Vol. 21, No. 9, 
1091-1104. 
Ruisheng Wang ve Yun Zhang, 2003.“Extraction of urban road 
network using Quickbird Pansharped Multispectral and 
Panchromatic Imagery by Performing Edge-Aided Post- 
Classification” 
http://www.geoict.net/Resources/Publications/RW_UNB_GRS 
S_2003.pdf 
Wiedemann, C., Hinz, S., 1999. “Automatic extraction and 
evaluation of road networks from satellite imagery”. 
International Archives of Photogrammetry and Remote Sensing, 
vol. 32, Part 3-2W5, pp. 95-100. 
Xiangyun Hu C. Vincent TaoYong Hu, 2004 “Automatic road 
extraction from dense urban area by integrated processing of 
high resolution imagery and lidar data” 
http://www.isprs.org/istanbul2004/comm3/papers/288.pdf 
Zheng, Jeffrey, Liu, Gang-Jun, and Coppa, Isabel. 1998. 
“Linear Geo-spatial feature extraction from remotely sensed 
digital imagery using conjugate transformation”. 9th Australian 
Remote Sensing and Photogrammetry Conference, vol. 1, pg 
6401.