The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
Figure 6 shows ED and MEAN statistics of nine land cover 
types. Generally speaking, bare ground shows the highest 
MEAN value, while water bodies show the lowest. In between 
there are numerous ‘confusing’ classes including three types of 
residential area, farmland, grassland and woodland. It is 
obvious that the MEAN alone does not provide enough variance 
to distinguish residential areas from their background. Old 
urban and rural residential areas show greater ED values as 
their buildings are assembled densely. Roads and cars on the 
road have clear edges resulting in a high ED value. New urban 
residential areas have sparse buildings, thus their ED values are 
smaller than the other two types of residential areas. Trees in 
woodland often show clear crown and shadows on high- 
resolution images, thus is more likely to detect edge point with 
this cover type. Other background classes such as water, 
grassland, bare farmland and bare ground have an ED value 
close to zero. Thus, it is clear that ED is a good candidate for 
the detection of residential areas with the background except 
road and wood land. 
Figure 7 presents the contrast group to distinguish residential 
areas and background. New urban residential area, road, rural 
residential area, old urban residential area and wood land show 
more contrast than water, grassland, bare farmland and bare 
ground. 
Figure 8 shows orderliness group in describing pixel variation. 
All three kinds of residential areas have less order or uniform 
surface: woodland and road show medium uniform surface, 
while water, grassland, bare farmland and bare ground represent 
a uniform surface. 
Figure 9 uses SD and COR to describe differences of 
residential areas and their background. It can be seen that new 
built urban residential area, old urban residential area, rural 
residential area, road and woodland all show greater SD values, 
which means that the pixel DN values are more diversified. In 
contrast, bare ground, grassland, water and barren farmland all 
show smaller SD values. All three types of residential areas are 
characterized with smaller COR values than the background 
classes, suggesting more discrete and less dependent spectral 
distribution. 
4. CONCLUSIONS 
In this paper, we investigated methods for selecting and 
evaluating texture parameters (window size, quantization level, 
displacement and orientation) for the identification of 
residential areas based on JM-distance. grey level co 
occurrence matrix (GLCM) and edge density (ED) approaches 
with candidate nine texture measurements (contrast, 
homogeneity, dissimilarity entropy, energy, mean, standard 
deviation, correlation and edge density) is selected as candidate 
texture measurements. The texture parameters are selected 
based on Jeffries-Matusita distance (JM distance) between 
residential and its background in corresponding texture space. 
IKONOS panchromatic imagery has been used as example and 
the optimal texture parameters were selected by using the 
proposed method. Further studies will be focused on the 
selection of optimal texture combination to improve the 
residential classification results. 
ACKNOWLEDGEMENT 
This project is financed by two items of the National Natural 
Science Foundation of China (Contract No. 40337055 and 
Contract No. 40501062). 
REFERENCES 
Anys, H. and He D.C., 1995. Evaluation of textural and 
multipolarization radar features for crop classification, IEEE 
Transactions of Geoscience and Remote Sensing, 33(5): 1170- 
1181. 
Cobum, C.A., Roberts, A.C.B., 2004. A multiscale texture 
analysis procedure for improved forest stand classification, 
International Journal of Remote Sensing, 20 October, 25(20), 
4287-4308. 
Conners, R.W and Harlow, C.A.,1978. Equal probability 
quantizing and texture analysis of radiographic images, 
Computer Graphics Image Processing, 8, 447-463. 
Canny J.F. 1986. A computational approach to edge detection. 
IEEE Transactions on Pattern Analysis and Machine 
Intelligence, 8(6): 679-698. 
Gong, P., 1991. A comparison of spatial feature extraction 
algorithms for land-use classification with SPOT HRV Data, 
Remote Sensing of Environment, 40, 137-151. 
Haralick, R.M. 1979. Statistical and structural approaches to 
texture, Proceedings of the IEEE, 67, 786-804. 
Karathanassi, V., Iossifidis, CH., and Rokos, D., 2000. A 
texture-based classification method for classifying built areas 
according to their density, International Journal of Remote 
Sensing, 21(9), 1807-1823. 
Myint, S.W. and Lam, N., 2005. A study of lacunarity-based 
texture analysis approaches to improve urban image 
classification, Computer, Environment and Urban Systems, 29, 
501-523. 
Shaban, M.,A., and Dikshit, O., 2001. Improvement of 
classification in urban areas by the use of textural features: the 
case study of Lucknow city, Uttar Pradesh, International 
Journal of Remote Sensing, 22(4), 565-593. 
Soh, L.K. and Tsatsoulis, C., 1999. Texture analysis of SAR sea 
ice imagery using grey level co-occurrence matrices, IEEE 
Transactions on Geoscience and Remote Sensing, 37(2), 780- 
795. 
Swain, P.H. and Davis S.M.,1978. Remote Sensing: The 
quantitative approach, New York: McGraw-Hill,