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A SIMPLIFIED FUSION METHOD BASED ON SYNTHETIC VARIABLE RATIO 
Pang Xinhua*, Xi Bin , Chen Luyao, Pan Yaozhong,, Zhuang Wei 
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University; College of 
Resources Science & Technology, Beijing Normal University, Beijing 100875, P. R. of China 
-(xhpang, binxi, lychen ,pyz, terfil)@ires.cn 
KEY WORDS: Remote Sensing, Data Fusion, SVR, SSVR 
ABSTRACT: 
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, multiplicative, Brovey transform and ISVR 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. 
1. INTRODUCTION 
With the rapid group of the internet and other electronic sources 
of information, the problem of the coherent merging of 
information from multiple sources has become an important 
issue. This problem has many manifestation ranging from data 
mining to information retrieval to multi-sensor fusion (Ronald, 
2004). For many applications the information provided by 
individual sensors are incomplete, inconsistent, or imprecise 
(Varshney, 1997; Hall et al, 1997; Pohl et al., 1998). 
Additional sources may provide complementary data, and 
fusion of different information can produce a better 
understanding of the observed site, by decreasing the 
uncertainty related to the single sources (Farina et al., 1996; 
Cl ement et al., 1993). 
In data fusion the information of a specific scene acquired by 
two or more sensors at the same time or separate times is 
combined to generate an interpretation of the scene not 
obtainable from a single sensor. Alternatively, data fusion is 
done to reduce the uncertainty associated with the data from 
individual sensors. Relaxing this operational definition slightly, 
also the combination of the information acquired by the same 
sensor at different times to improve interpretation is considered 
as data fusion (Tax et al., 1997). Image fusion is used to merge 
images of different spatial and spectral resolutions to create a 
high spatial resolution multi-spectral combination. High 
spectral resolution allows identification of materials in the 
scene, while high spatial resolution locates those materials. 
The actual fusion process can take place at different levels 
(pixel-level, feature-level and decision-level) of information 
representation (Pohl et al., 1998). Which level to choose is 
determined by the purpose. For example, pixel-level fusion is 
appropriate for land use classifications; because pixel-level 
fusion can maintain more spectral characteristics of the original 
multi-spectral image and decreasing the obscurity of image 
interpretation. The common pixel-level fusion methods are 
PC A, Multiplicative, Brovey Transform, HPF Transform, HIS 
Transform, HDF Transform and wavelet Transform. But these 
methods may cause spectral distorting and are difficult to 
accomplish. 
Synthetic Variable Ratio(SVR) fusion method was presented by 
Munechika et al., improved by Zhang(Zhang, 1999; Zhang, 
2001). But it is still difficult to calculate. The main purpose of 
the paper is to present a simplified fusion method with physical 
meaning based on SVR. The main purpose of the paper is to 
present a simplified fusion method with physical meaning based 
on SVR. 
2. SSVR FUSION METHOD 
2.1 SVR (Synthetic Variable Ratio) Fusion Method & 
Improvement 
SVR is proposed by Munechika et al (1993) taken example of 
TM (30m) - SPOT (10m), and the formulation is: 
XSP i = Pan H 
(1) 
here XSP' means band i grey value of high spatial resolution 
image after fusion, Pan H means grey value of original high 
resolution spatial panchromatic image, XS Li means band i 
gray value of original low spatial resolution multispectral image, 
and Pan, means grey value of high resolution spatial 
Lsyn 
panchromatic image that are synthesized by band 1, 2, 3, 4 of 
multispectral TM image. 
* Pang Xinhua, male, Master, major in GIS&RS, College of Resources Science & Technology, Beijing Normal University, 
Beijing ,China. Recent research interests in the applications of remote sensing in crop planting area measurement and land use/cover 
change detection. Tel: 010-58805750, E-mail: xhpang@ires.cn