The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part BI. Beijing 2008 
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269 input images with 65 X 65 pixels cropped from IRS-C source 
images are used for test. Their signal-noise ratio is between 0.3 — 
0.5, and different scene area is covered. Signal-noise ratio between 
input image and its corresponding SPOT image is calculated, 
shown in Table 1 and Figure 3. 
Table 1. The relationship between signal-noise ratio from 0.3 to 0.5 
and rate of successful matching 
Signal-noise 
ratio 
Number of 
input images 
Number of 
correct 
matching 
Rate of 
successful 
matching (%) 
0.30-0.32 
16 
9 
56.25 
0.32-0.34 
18 
12 
66.67 
0.34-0.36 
21 
18 
85.71 
0.36-0.38 
34 
21 
61.76 
0.38-0.40 
45 
36 
80.00 
0.40-0.42 
28 
25 
89.28 
0.42-0.44 
29 
24 
82.76 
0.44-0.46 
27 
24 
88.89 
0.46-0.48 
27 
25 
92.59 
0.48-0.50 
24 
24 
100.0 
4.2 Summation of Image Gradient 
Image gradient reflects image’s information content and the 
amount of features the image contained, and it is the key factor for 
feature-based matching. The value of gradient is great when image 
contains rich prominent features. However, the value of gradient is 
small for flat area, and it is zero for the area, whose grey-level is 
invariable. Gradient calculator in common use is Robert, Prewitt, 
Krisch arithmetic, etc. Following is an example for calculating 
gradient located at (x, y) for image I(x, y) using Sobel arithmetic, 
viz.: 
G x = (/(x - \,y +1) + 2/(x,y +1) + I(x + l,y +1) 
- (7(x -1, y -1) + 2/(x, y -1) + /(x +1, y -1)) 
G y = (/(x - l,y -1) + 21 (x -1 ,y) + /(x - l,y +1) 
- (/(x +1, y -1) + 21 (x +1 ,y) + /(x +1, y +1)) 
Gradient at (x,y) for image I(x,y) is a vector, as follows: 
V/ = 
Gx 
dl_ 
dx 
a/ 
dy 
(9) 
Magnitude of gradient is mag(VI) = \G X + 
From curve’s trend in Figure 3, we can see that the greater the 
signal-noise ratio is, the greater the success-matching rate is in the 
whole. But, there is some exception, for example, success 
matching rate for images, whose signal-noise ratio is from 0.32 to 
0.36, is greater than that of images whose signal-noise ratio is from 
0.36 to 0.38. It is shown that images’ signal-noise ratio has no 
strong relation to the success-matching rate for mutual information 
based method. It is obvious that mutual information expresses 
statistical characteristic of image’s grey value, good matching 
result can still be obtained when non-linear change of image’s grey 
value is taking place and images to be matched have lower signal- 
noise ratio value. 
The value of magnitude of gradient at very pixel is added together, 
and then the summation of image gradient for whole image is 
derived. It shows edges contained in the image and the change of 
image’s grey-level. Therefore, summation of image gradient can 
represent image’s information content. In this paper, experiment is 
carried out for the relationship between the summations of image 
gradient and success rate based on mutual information matching 
method. It is also found out that there is no strong relation between 
image gradient and matching success rate using mutual information 
method. 267 input images, whose image gradient is from 4.0 e+005 
to 6.0e+005, are used to test and detail statistic is obtained, shown 
in Table 2 and Figure 4. 
The curve in Figure 4 shows that image’s gradient magnitude has 
Rate of successful matching 
4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 
image gradient 
Figure 4. Success rate of matching for images whose summation of gradient magnitude is from 
4.0e+005 to 6.0 e+005