The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
Image 
CC of 
Bandl 
CC of 
Bandl 
CC of 
Bandl 
Average CC 
MLT 
0.9051 
0.8754 
0.9222 
0.9009 
MB 
0.9193 
0.9349 
0.9345 
0.9296 
HPF 
0.8899 
0.8861 
0.8956 
0.8905 
SFIM 
0.9358 
0.9331 
0.9447 
0.9379 
Table 3. Correlation Coefficient of various fused images with MS 
image 
4.2.1 Spectral Fidelity: Table 1 shows the BM of MS and 
fused images. It clearly indicates that the BM of SFIM is the 
minimum, and BM of MB is the second minimum. Table 3 shows 
that The CC of SFIM-fused image is 0.9379, which is the highest 
in the four algorithms. HPF is the minimum. According to the BM 
and CC, we can see that the SFIM-fused image has the maximal 
relativity with MS image. So SFIM is the best method in retaining 
spectral property of the original image among the four used 
methods, and MB takes second place. 
4.2.2 High Spatial Frequency Information Absorption: 
Table 2 shows the Entropy and means of each band MS and fused 
images. The Average Entropy of SFIM is the highest in the four 
algorithms, band 1 and band 3 is also the highest of all. The 
Entropy can reflect the average information included in the fused 
image, therefore, the SFIM-fused image has absorbed the high 
spatial frequency information most and thus shows crisper than 
the others (Figure. 1). The other three are not much different but 
the HPF is a little more in information than MLT and MB. 
4.2.3 Definition of Image: Table 1 shows the SD, AG of MS 
and fused images. SD reflects the change in details of fused image, 
and AG reveals the change of values between the pixels border 
upon, namely reflects the definition of image. It evidently 
indicates that the SFIM is the highest either in the SD, or in AG. 
SD of MLT is the second highest, and MB takes second place in 
AG. Therefore, SFIM-fused image is more legible than other 
three algorithms. 
Finally, from the above analysis and comparison, It is summarized 
that the SFIM-fused image has the best spectral fidelity and 
definition, and absorbs the high spatial frequency information 
most. It is a fusion technique based on a simplified solar radiation 
and land surface reflection model. By using a ratio between a 
higher resolution image and its low pass filtered (with a 
smoothing filter) image, spatial details can be modulated to a co 
registered lower resolution multi-spectral image without altering 
its spectral properties and contrast. The technique can be applied 
to improve spatial resolution for either colour composites or 
individual bands. So it is superior to the other three methods for 
QuickBird images. The MB takes second place in BM, AG and 
CC. So it can also be called a good method for the imagery fusion. 
The HPF can absorb the spatial information commendably but is 
bad in spectral fidelity. The MLT is the worst for QuickBird 
imagery. 5 
5. CONCLUSIONS AND PROSPECT 
5.1 Conclusions 
(1) The comparison of the SFIM with MLT, HPF and MB shows 
that the SFIM-fused image has the best definition as well as 
spectral fidelity, and is the best in high textural information 
absorption. Therefore it is the best method for QuickBird 
image fusion in the four algorithms and MB takes second 
place. 
(2) SFIM is a simple but superior fusion algorithm and the 
time of computing is short, so it is suited for the image fusion 
which covers a large-scale area. 
5.2 Prospect 
(1) In order to find out the fusion algorithm which is suited 
for QuickBird images fusion, this study selected MLT, MB, 
HPF and SFIM as the tested methods. Through the four 
algorithms are the representative at pixel level fusion, it is 
unilateral. In the subsequent research, we should test more 
fusion algorithms and discovers the best method for 
QuickBird images. 
(2) This study only selects the five common evaluation 
criteria such as BM, CC, Entropy, SD and AG. Although it 
covers the most image evaluation field, it has some 
insufficiency. In the following research, we should adopt 
more indexes to assess fusion result comprehensively from 
other aspect, such as image classification. 
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