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image, with a comprehensive considering of the subjective and 
objective evaluation methods. 
2.3 Structural Similarity 
During observing images, what the human eyes practically 
extracted are not the error between image pixels but the image 
structure information. And, human visual system can self- 
adaptively extract the structure information in image 
background. At present, many researchers have pointed out that 
the structure distortion is the most important factor in image 
quality assessment. This viewpoint has given a new direction 
for now image quality assessment research, and already 
achieved much research production (Wang, 2002; Xydaes, 
2000; Di, 2006). This paper adopted the formulation as 
following to describe the structure similarity between the 
original and fusion images. 
Suppose A, B represents respectively the original and fusion 
image, then the structure similarity (SS) between them will be 
defined as: 
SS AB ~ L A b ’ Cab ' S ab ~ 
2 MaMb 2 °a°b °ab (1) 
MA + Mb ^B ®A®B 
Where, there are: 
In the above formula, p A and u B are respectively the mean 
value of the image window; o 2 A and a 2 B are respectively the 
variance of the image window; a AB is the covariance of A and 
B image window data; L AB , C AB , S AB is respectively the 
compare items of lightness, contrast, and structure between the 
two images, with values between 0 and 1. 
The structure similarity value is between [-1.1], and with a 
value close to 1, the fusion image will have a higher quality. It 
combines the image structure information and human visual 
system characteristic to evaluate the fusion image quality, with 
a better effect than the other subjective or objective index 
separately. 
3. THE COMPREHENSIVE EVALUATION MODEL 
FOR THE FUSION IMAGE 
In the processing of fusion image quality evaluation, we must 
consider the relationship among the original image A and B 
and the fusion image F, to construct the corresponding 
evaluation index, because the fusion result image derives of the 
two original remote sensing data. In this paper, we adopt the 
construction function E (A, B, F) to evaluate the fusion image 
quality synthetically (Hu, 2004), which is as following: 
E(A,B,F)=X a SS af +X b SS bp 
; = s a(v) (3) 
s A {o))+s B {(o) 
A B = 1 —A A 
Where, A A and A B are respectively the SS weight value of the 
original and fusion images; S A («) and S B («) are the variance 
value of the original images A and B. The above formula can 
synthetically evaluate the structure similarity between the 
fusion and original images. But, in view of the characteristics 
of high-resolution airborne SAR and SPOT5 images, the 
human visual system will have different visual levels for 
different ground object types, especially for the trees, building, 
water, road, land cover and mountain shadow and so on. 
Considering this, we put forward a kind of comprehensive 
fusion quality (CFQ) evaluation model, according to the 
interpreting characteristic of the different ground object types, 
with a formulation as: 
CFQ = fl& k E k (A,B,F)=il 6 t [i,® „ (*)+ X,SS tp (*)] (4) 
k = 1,2,3 K 
Where, K represents the sum number of types discussed in the 
fusion result image; 0 k is the corresponding weight value for 
each ground object type. 
4. TEST AND RESULT ANALYSIS 
In this paper, we take high-resolution airborne SAR, with a 
resolution of 1 meter, and 10-meter resolution multi-spectral 
SPOT5 images as the test dataset. And, the latter may 
combined band 4, 2 and 1 into a pseudo-colour image. The 
fusion image of airborne SAR and SPOT5 data has just shown 
in Figure 1. 
Figure 1 the fusion result image from airborne SAR and 
SPOT5 
During the data processing, we chose 5 types of ground objects 
which were often applied in many practical fields, involving 
buildings, trees, water, road, and land use. From each type, we 
take 3 samples in the original and fusion result images, 
utilizing ERDAS software function to obtain the statistical 
mean value and variance values for each sample window. So, 
we can discuss the different fusion effect for each type of 
ground objects, and get the final fusion quality evaluation using 
the CFQ model described as the above. And, the SS and test 
result for each type has been seen in Table 1 as following: 
type 
SAR 
SS 
0.9389 
E 
CFQ 
0.804 
Building 
SPOT5 
SAR 
0.926 
0.7338 
0.9328 
trees 
SPOT5 
0.826 
0.7777 
Land use 
SAR 
0.8689 
0.896