The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B4. Beijing 2008 
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Window size 
Figure 2. JM-distance between residential and background 
classes on different textural features with respect to window 
size 
3.2 Optimal parameter value of quantization 
Four different quantization schemes 16, 32, 64 and 256 grey 
levels were tested for GLCM texture features. Figure 3 shows 
JM-distance variation with respect to the pixel grey levels. The 
result shows that the quantization level dose not have 
significant impact on CON, DIS and SD bands, and appears to 
have negative impact on the other texture features including 
ENT, HOMO, ASM and COR. To balance the accuracy and 
demand on computation resources, we chose a quantization 
level of 32. 
Quantization level 
Figure 3. JM-distance between residential and background 
classes on different textural feature with respect to quantization 
level 
3.3 Optimal parameter value of displacements 
In this study, displacements of 1,3,5 and 7 pixels were tested as 
shown in Figure 4. In general there is a tendency that JM- 
distance decreases with the increasing displacement. Since it 
appears that low displacement yield generally better results 
(greater JM-distances) for most texture bands, we therefore 
selected one as displacement in texture computation. 
Figure 4. JM-distance values between residential class and 
background class on different textural feature image with 
respect to displacement 
3.4 Optimal parameter value of orientation 
To assess the impact of various orientation on the derived 
texture measures, we examined their separability with the 
variations on orientation of 0°, 45°, 90°, 135° and the average 
of four orientations. Figure 5 shows that, except for SD, JM- 
distances on average orientation are significantly greater than 
on the others. 
Texture features 
Figure 5. JM-distance values between residential and 
background classes on different textural features with respect to 
orientation 
Table 1 summarizes the optimal parameters selected for 
deriving texture measures in this study. 
As the parameters of each texture feature have been selected, 
nine texture images can be obtained from the original IKONOS 
Panchromatic image. The nine texture features can be separated 
into five categories according to their properties, namely, the 
‘contrast’ group (CON, DIS and HOMO), the ‘orderliness’ 
group (ASM and ENT) , the ‘edge’ group (ED) , the ‘mean’ 
group (MEAN) and the ‘descriptive statistics’ group (SD and 
COR). To understand the distribution of different kinds of land 
cover types on each texture space image better, we plot the 
clusters based on the training samples. Figure 6 to figure 9 
shows the cluster result.