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An example would help to show a GLCM from a 5x5 pixel 
window with Spatial = (0,1) (i.e. Nbr is one pixel below the Ref 
pixel). 
Image Window Resulting co-occurrence 
matrix 
Pixel value 
  
  
  
  
  
21716015 149 114 doopbrog 
¥ 9 Den Q 
S 47/8 }5 = #10" 
S. 510090 712 
Sa 333 pz qs = 6 010-040 
TT A É 7919] 91 
Sq 1 00180 
6 17141814 
  
  
  
  
  
  
  
The computation starts from the top left corner and counts the 
occurrences of each reference pixel (Ref) to neighbour pixel 
(Nbr) relationship. For example, the first relationship is 3 (Ref) 
to 5 (Nbr). The second relationship is 7 (Ref) to 4 (Nbr). The 
co-occurrence matrix lists the number of occurrences of each 
relationship. Hence in the example, the (Ref,Nbr)=(4,6) 
relationship occurs twice, and the (3,5) relationship occurs 
once. So the (4,6) element of the GLCM is set at 2 while the 
(3,5) element is set to 1. Such a GLCM is asymmetric. A 
symmetric GLCM, which is the sum of the asymmetric co- 
occurrence matrix with its transpose, can also be used. The four 
symmetric measures of texture used in this study are: 
1) Contrast: It is a measure of the amount of local 
variation in the image. 
i)Entropy: It is high when the elements of GLCM 
have relatively equal values. Low when the 
image is uniform in the window. 
ill)Angular Second Moment (ASM): This is the 
opposite of Entropy. It is high when the 
GLCM has few entries of large magnitude, 
low when all entries are almost equal. This 
is a measure of local homogeneity. 
iv)Correlation: Measures the linear dependency of 
grey levels of neighbouring pixels. 
  
The computations of these are done by the following equations: 
If N is the number of grey levels; P is the normalized 
symmetric GLCM of dimension N x N then, 
P(1,) is the normalized co-occurrence matrix such that 
N-1 
> P(i,;))=1 
1,j=0 
N-1 
Contrast: 2 (P(i,))*(i-j)**2) 
ij=0 
N-1 
Entropy: 2 (-P(i,j))*In(P(i,))) 
1,3=0 
assuming that 0*In(0) = 0 
NI 
ASM: 2. (1,750, N- 1(P(1,) **2) 
1,1=0 
N-1 
Correlation: X (PCi.j)*G-D)*G-p)Y V (9;*6) 
1J=0 
   
3. STUDY AREA AND DATA USED 
The study area is Surendranagar district, Gujarat state situated 
in western India between 22? 08"N to 23? 3I"N and 70° 58°E to 
72? 8'E. This district has a mean annual rainfall of 507 mm but 
only 20 percent of the district follows irrigated agriculture. The 
soil type is black cotton. During the monsoon season the 
predominantly grown crop is cotton, followed by pearl millet 
and rice. These crops are sown generally by the second 
fortnight of June to early July. Other vegetation is scrub. 
3.1 Data Used 
Radarsat Standard Beam 5 in descending node was taken for 
the study. It is HH polarised, nominal incidence angle is 38 
degrees and operates in C band at 5.6 Ghz. The pixel spacing 
was 12.5 m. Multi-temporal data (three subsequent dates) was 
acquired. The dates of satellite pass were on July 18, Aug 11 
and Sep 04, 2001. This data covered the early growth to 
vegetative phase of the crops. Raw data, which was only 
calibrated and georeferenced, was used for the study. 
The ground truth was done synchronous with each pass of the 
satellite and for better location accuracy, a hand held Global 
Positioning System was used. Plant parameters like age, height, 
vigour, density, LAI, row direction and spacing and soil 
parameters like moisture status, type, roughness, weed 
infestation and field bund utilisation were collected. 
The analysis was done using EASI/PACE software on Octane 
workstation. 
4. METHODOLOGY 
The multi-temporal data acquired was calibrated and 
georeferenced. The data was calibrated using header 
information. The header information was used to compute the 
backscatter from the digital number value by computing the 
radar brightness and incidence angle for each pixel and to 
create the dB image. The digital numbers were converted first 
to B° using the calibration coefficients provided in the leader 
file of the data using the following: 
B° = 10 * logo [(DN; + A3) / A2;] dB 
The B° was then converted to 6° using the following : 
0, i fi; +10 * log;o(sinl;) dB 
Where, A2 is the scaling gain value for the j'" pixel and A3 is 
the fixed offset and I; is the incidence angle at the i™ range 
pixel. 
No filtering was done. The four texture measures were chosen 
from available literature. Five window sizes were so chosen so 
as to find out the feasibility of feature discrimination and 
efficiency of texture based classification based on window 
statistics. The window sizes chosen were 3 x 3; 7 x 7; 15 x 15: 
25 x 25 and 31 x 31. The vegetation classes chosen were 
cotton, rice, pearl millet, scrub. In addition, urban and water 
sites were also taken. Texture measures were used on all three- 
date data separately and the output images were put on 32-bit 
real channel. The statistics obtained from output image were 
used as input for computing the spectral distances using