224 
Index 
Methods 
Band 
PCA 
Multiplicative 
Brovey 
ISVR 
SSVR 
2 Green 
4.7319 
5.2813 
5.7992 
5.3906 
5.6933 
Entropy 
3 Red 
5.2046 
5.5060 
6.0852 
5.9295 
6.1714 
4 Near IR 
6.1444 
5.7775 
6.1940 
6.1071 
6.3436 
Correlative 
2 Green 
0.655098 
0.750773 
0.494990 
0.883070 
0.884670 
Coefficient 
3 Red 
0.642385 
0.816172 
0.810196 
0.917164 
0.926713 
4 Near IR 
0.829165 
0.808885 
0.910622 
0.924181 
0.937237 
Table 2 Comparisons of different fusion methods 
As incapability of comparison of different resolution of fused 
image and the image before fusion, the multi-spectral image 
and the pan image are firstly degraded to 60m and 30m 
respectively, which makes the fusion resolution of the result 
30m, then the fusion is carried up, finally the we could get the 
result of comparison of the fusion image and the original image, 
so are the entropy and coefficient, seen in Table 2. 
From the aspect of result of data fusion, the image information 
get from the SSVR is the largest. According to the two 
indicators, SSVR algorithm not only enlarge the quantity of 
information of multi-spectral data, but also it protects the 
spectral information contain in the original image. It could 
draw an conclusion that SSVR is suit for the advanced image 
process such as classification. 
5. CONCLUSION AND DISSCUSION 
In this paper, a Simplified Synthetic Variable Ratio (SSVR) 
fusion method is presented to merge high spatial resolution 
panchromatic (Pan) image and high spectral resolution 
multispectral (MS) images based on a simulation of the 
panchromatic image from the multispectral bands. Landsat7 
ETM + images were used to assess the effectiveness of 
classification-oriented SSVR method in comparison to 
Principal Component Analysis, multiplicative, and Brovey 
transform methods. 
Compared to other fusion methods, the images generated by 
SSVR method have more information and high spatial 
resolution while maintaining the basic spectral characteristic of 
the original multispectral image, and SSVR method is simpler 
to carry out than other SVR methods. The SSVR method is a 
good fusion method since its result achieves a good spectral 
quality and easy to carry out. In recent studies, there is no 
unified fusion standard evaluation effect, which is an urgent 
problem to be solved. The shortage is that the method is 
rigorous to remote sensing data because it achieves a good 
result only when Pan Images has almost the same spectrum 
range with MS images. 
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