Figure 2. Gray Level Co-occurrence Matrix 
Gray level co-occurrence matrix can be expressed by 
mathematical formula as follows: 
P(iJ,S,0) = {[(x,y), (x+Ax,y+Ay)] / f(x,y) = i 
f(x+Ax, y+Ay) -j; x = 0,1,2, ..., Nx-1; y-0,1,2, ...,Ny-l} 
In the formula, i, j=0, 1, 2... L-l; x, y is pixels coordinates in 
the image; L means gray progression of the image; Nx and Ny 
respectively enumerate the number of image line. 
According to the above definition, P (i,j,d,0) expressed 
elements with Line i and Row j of the gray level co-occurrence 
matrix. It also indicated appearance frequencies on all the pixel 
point pairs with the direction as 9 and distance as S. In addition, 
a pair of pixel point includes one gray i value and gray j value. 
9 here included angle created by two pixels and x axis 
according to the clockwise direction. 9 value is 0°, 45°, 90°and 
135°. Definitions on different 9 matrix element are as follows: 
P(i, j, 5, 0°) = # / ((k,l),(m,n)) E(Ly*Lx) x(Ly*Lx) / k-m=0, 
/ l-n / =S; I(k, z)=i,I(m, n)=j / 
P(i,j,d,45°)=# / ((k,l),(m,n)) <E(Ly*Lx)x(LyxLx) / (k-m=S,l- 
n=-S); Or(k-m=-d,l-n=S), l(k, l)=i,l(m, n)=j / 
P(iJ,S, 90°) = # / ((k,l),(m,n)) E(LyxLx) x(Ly*Lx)//le 
nt / =d,l-n-0; I(k, l)=i,I(m, n)=j / 
P(i,j,d,135°)=# / ((k,l),(m,n)) (E(LyxLx)x(LyxLx) /(k-m=d,l- 
n=d); or (k-m=-S,l-n=-d), I(k, l)=i,I(m, n)=j / 
In the formula, symbol # / X / expresses elements numbers of 
gathers X. 
The gray level co-occurrence matrix reflects integrated 
information image gray on direction and adjacent interval and 
change scope, which is the base to analyze partial model 
structure and array rule of image. Feature quantity as texture 
analysis is not usually gray level co-occurrence matrix applying 
computing directly. It extracts texture feature quantity based on 
gray level co-occurrence matrix which is called second statistic 
summation. 
Using the gray level co-occurrence matrix, amount of 
texture feature can be defined whose aim is to assistant 
classification of sensing image texture. Haralick(1973) 
proposed 14 textural eigenvalue. However, Barald thought that 
computation quantity of grey level co-occurrence is great and 
following four features are often adopted to extract texture 
feature of image. For sensing image, four kinds of statistic 
quantity what follows will have the best effects[Guo, 2005]. 
These are four mentioned textural features 
ASM-XX A/,./) 2 ; 
i j 
C0N= XX<''-iTW,./) ; 
' j 
CORRLN= 
II ((ij)p(Uj))~M x M y 
!<J G 
y ' 
EMT=- X X PO’j) log pi}, j) ■■ 
3.2 Extraction of the textures features image based on the 
gray level co-occurrence matrix 
3.2.1 The technical plan about extraction of texture 
feature image 
The extraction algorithm of textures feature image based on 
gray level co-occurrence matrix is as follows: 
1) Transform the colored image into the gray image and pick 
single band of the gray image. 
The texture feature of the image is extracted in term of the 
single band. Therefore, the first step of computing texture 
feature is that single band standing for R or G or B are 
extracted from multi-band and then one of the bands are chosen 
to compute the texture feature. Because the texture feature is 
one kind of structure feature, using the texture feature image 
with different bands will produce the same texture features. 
Thus we choose the R band to take the research. In figure 3, 
remote sensing image with the single band is picked through 
matlab. 
Figure 3. The remote sensing image of R band 
2) Gray level quantification. Since computing quantity is great, 
we should cut down the gray levels under maintaining primary 
shape of the image to save time. So, when computing gray level 
co-occurrence matrix, the gray levels of original image is 
reduced to smaller scope. We usually extract level 8 or level 16 
to decrease co-occurrence matrix and save time. In this paper, 
the image is compressed into 8 gray levels. Although quantified 
image has some distortion, its influence for the texture feature 
is not too great. 
3) Computation requests of texture feature value 
a) Choice of sliding window 
Texture analysis method of the gray level co-occurrence matrix 
need choose the sliding window. That is the image is divided 
into several sub-images computing the gray co-occurrence 
matrices with size as windows. In this paper, we use two kinds 
of sliding windows with 5x5 and 7x7 to compute the texture 
feature value. 
b) Step of distance choice 
Use step d=l is appropriate through the experiment and the 
comparison. 
c) Direction choice 
The direction of compute gray level co-occurrence matrix 
always adopt 0°, 45°, 90° and 135°. We should handle the 
value of the four directions otherwise there are too many 
texture features which is not propitious to use. Average value 
of four directions is used as final texture feature value in the 
paper. 
4) Transform calculated texture characteristic value into the 
image, namely texture feature image.