The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B4. Beijing 2008 
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CON=TTP(i~j) 2 
‘ j 
HOMO=IZ-^Ar 
ijHHf 
DIS=X Z | i~j\P{ i J ) 
i j 
ENT-X Z P{ i »y )[— log P{ij)~\ 
i j 
' ASM=YTP 2 {i,j) 
i j 
MEAN=Y'Li*P(i,j) 
i j 
SD= lYXP(i-MEAN) Z 
(i-MEAN)( j-MEAN) 
2T 
cor=xx 
i j 
SD< 
(1) 
Where Z and j are two different grey levels of the image, P is 
the number of the co-appearance of grey levels Z and j . 
Edge density {ED) is usually computed through the number of 
edge pixels in a given window divided by the window size 
(Equation 2). The detection of edge pixels is the key issue in 
ED computing. There are a variety of algorithms have been 
proposed for edge detection. Among them, Canny edge 
detection operator (Canny, 1986) is one of effective method. It 
formulated edge detection as an optimization problem and 
defines an optimal filter, which can be efficiently approximated 
by the first derivative of Gaussian function in the one- 
dimension case. In this study, edge detection by the Canny edge 
detection operator was performed and a binary edge image 
(edge pixel is coded as ‘1’, and non-edge pixel is coded as ‘0’) 
was produced. Then, ED can be computed based on the binary 
edge image. 
i w/2 w/2 
ED(x,y)=— X Z g(x+k,y+l) 
w k=-w/2l--w/2 
(2) 
Where W is the window size. g(x,y) is the pixel value in the 
given window. 
the textural features whose differences will be used to 
characterize various class types need to be extracted over a 
local area of unknown size and shape. If this information is 
gathered over areas that are not large enough with respect to the 
texture elements or variations, then one cannot expect these 
local analyses to provide feature values that are invariant across 
the textured region. Consequently, it is desirable to extract the 
textural information over as larger an area as possible. If this is 
the case (i.e. texture features are not calculated from a single 
texture class), the features would be representing a hybrid 
values. This problem is similar to mixed-pixel problem and may 
be termed as mixed-texel problem (Shaban and Dikshit, 2001). 
Therefore, the need for a large window size results in a trade 
off between large window sizes that give stable texture 
measures and the increasing proportion of between-class 
variance texture pixels such large windows produce. 
2.2.2 Quantization level 
The dimension of a GLCM is determined by the maximum gray 
value of the pixel. The more levels included in the computation, 
the more accurate the extracted textural information, of course, 
a subsequent increased computation cost (Soh and Tsatsoulis, 
1999). Some of the major quantization schemes are uniform 
quantization, Gaussian quantization and equal probability 
quantization. The uniform quantization scheme is the simplest, 
in which gray levels are quantized into separate bins with 
uniform tolerance limits with no regard to the gray level 
distribution of the image. This technique is not always 
preferable. The Gaussian quantization technique is one such 
scheme. The grey level distribution of the original image is 
assumed to behave normally. Each quantization bin has the 
same area under the curve and thus different space smaller 
spaces in the middle of the distribution and larger spaces at the 
tails of the distribution. In the equal probability quantization 
scheme, each bin has similar probability and it has been shown 
to represent accurate representation of the original image in 
terms of textural based on GLCM (Conners and Harlow 
1978).The Guassian quantization scheme assume a Gaussian 
grey level distribution, which is not always true for high- 
resolution imagery. Equal probability quantization normalizes 
different image samples so that a bright feature and a dark 
feature, given the same texture, would have the same co 
occurrence matrix, which is undesirable since grey value is 
important in residential analysis. Thus, in our experiment, we 
have focused on the uniform quantization scheme. 
2.2.3 Displacement 
The displacement parameter^ is important in computation of 
GLCM. Applying large displacement value to a fine texture 
would yield a GLCM that does not capture detailed textural 
information, and vice versa (Soh Tsatsoulis, 1999). 
2.2 Parameters effect on texture 
Although absolute values of texture features had little meaning, 
it was worthwhile to understand how each feature varied with 
varying parameters given an image acquisition configuration. 
This should provide users with the knowledge with which to 
make a good selection of parameter values instead of testing all 
possible combinations. The following four parameters need to 
be pre-set for designing the texture features introduced above. 
2.2.1 Window size 
The moving window size used to calculate texture is a key 
parameter. In texture analysis, one of the main problems is that 
2.2.4 Orientation 
Every pixel has eight neighboring pixels allowing eight choices 
for 9 , which are 0°, 45°, 90°, 135°, 180°,225°, 270°or 315°. 
However, taking into consideration the definition of GLCM, the 
co-occurring pairs obtained by choosing 6 equal to 0°would be 
similar to those obtained by choose 0 equal to 180°. This 
concept extends to 45°, 90° and 135° as well. Hence, one has 
four choices to select the value of 9 . Sometimes, when the 
image is isotropic, or directional information is not required, 
one can obtain isotropic GLCM by integration over all angles.