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b. Using contrast feature c. Using Dissimilarity feature 
Figure 5. Extraction of residential area texture feature 
Figure 4 b and figure 5 b, c respectively represent the 
residential area from the below texture images. We can use the 
same texture analysis methods to extract the edge of residential 
area feature. 
43 Image Interpretation 
Different surface features exhibit different scattering 
characteristics. From the view of tone, urban areas put up very 
strong backscatter; forest puts up medium backscatter; calm 
water puts up smooth surface and low backscatter; rough sca 
puts up increased backscatter due to wind and current effects. 
In the above figure, we can see some mapping elements through 
the testing AIRSAR data, such as road, water area and 
residential area. 
  
  
Figure 6. Mapping element extraction 
In figure 6, we make our efforts to take mapping element 
extraction. Results show that extracting basic mapping element 
is feasible. 
163 
5. CONCLUSIONS 
The purpose of this study is twofold: first, to demonstrate the 
advantages of using AIRSAR data for topographic mapping 
purposes. Second, to demonstrate the advantages of utilizing the 
high resolution (0.5-meter) AIRSAR sensor data acquired for 
surface texture analysis and interpretation purposes. Finally, 
application of SAR images gives satisfactory results from above 
experiments. 
At present, with the speedy development of China, the timely 
repairing and updating map, establishing periodically updating 
geography databases, dynamic monitoring land use change 
conditions, and deriving various kinds of latest thematic map 
are the imperative problems. The first important factor that 
restricts this kind of dynamic monitoring is whether we can 
provide the practicable, high-resolution, continuously stable and 
rapidly receiving and useful data sources or not. [t is shown that 
AIRSAR with its full-time and all-weather characteristic 
becomes optimum remote sensing data sources solving the 
tradition difficulty district in the topography of Surveying and 
Mapping. 
The various methods for modelling textures and extracting 
texture features can be applied in four broad categories of 
problems: texture segmentation, texture classification, texture 
synthesis, and shape from texture. From the above study, we 
could perform texture classification through identifying some 
types of homogeneous regions, and texture segmentation 
through finding the texture boundaries. 
6. ACKNOWLEDGMENTS 
The testing AIRSAR image data are provided by 38th institute 
and CATIC SIWEL 
7. REFERENCES 
Du, L. I, 1990. Texture Segmentation of SAR Images Using 
Localized Spatial Filtering, [International Geoscience and 
Remote Sensing Symposium, 4, pp. 1983-1986. 
Jiang, Q. X., Liu H. P., KONG L. Y., 2003. The Application of 
Texture Analysis in TM Image Information Extraction, Remote 
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Lee, J. H. and W. D. Philpot, 1990. A Spectral- Textural 
Classifier for Digital Imagery, International Geoscience and 
Remote Sensing Symposium, pp. 2005-2008. 
Mihran, T., 1998. The Handbook of Pattern Recognition and 
Computer Vision, pp. 207-248. 
Rignot, E. and Kwok, R., 1990. Extraction of Textural Features 
in SAR Images: Statistical Model and Sensitivity, /nternational 
Geoscience and Remote Sensing Symposium, pp. 1979-1982. 
Yang X. M., Zhou C. H., 1998. Report on ACRS "Recognition 
of Flooded Arca in Radar Image using Texture Feature 
Analysis”, Manila. 
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| (accessed 6 May. 2004)