The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
1264 
image, (2) is the SPOT image, (3), (4) is the fusion image in 
WT method when wavelet transformation exponent number J=l, 
2, (5) fusion image which obtains for this article method. 
From the chart we may see, the result basically maintained the 
original map spectral characteristic regarding WT method when 
J=l, but image spatial detail performance ability is not high, the 
entire image is quite dim, the edge characteristic is not very 
clear like rivers edges, the enhancement effect is not very good. 
Regarding the result of WT method J=2 fusion, the ringing 
effect appeared, to create the fuzzy texture. By the method 
proposed in this paper we obtained the image (5) in which not 
only the outline is quite clear but the river boundaries is clearer 
than (3) and (4). Moreover the spectral characteristic maintains 
quite well. 
When we carry on the quota analysis comparison, we use the 
correlation coefficient and spectrum tortuosity of the image to 
carry on the appraisal. Table 1 gives the parameter contrast of 
the image fusion results, and it can be easy to see from the 
parameters in the table that the method proposed in this paper 
has a very good effect in enhancing the spatial texture 
characteristic of multi-spectrum images and in maintaining 
spectrum information. 
Method 
types 
spectrum 
tortuosity 
Correlation coefficient 
SPOT 
Image 
TM multi- 
spectral 
Image 
Method 
J=1 
WT 
19.03 
0.61 
0.79 
Method 
J=2 
WT 
24.42 
0.72 
0.74 
The 
method 
new 
24.05 
0.83 
0.74 
Table 1 Comparison of images fusion result 
4. MULTI-SPECTRUM IMAGE FUSION BASED ON 
WAVELET TRANSFORMATION AND VISUAL 
CHARACTERISTICS 
Although the fusion effect of wavelet transformation method is 
relatively ideal, its transformation restructuring is actually a 
process of the high pass and the low pass filter, and, to a certain 
extent, loses some edge information in the primitive image, as a 
result of which the ringing effect in fusion image appears. 
Therefore, when the image fusion is carried on, the wavelet 
transformation and other alternative means are often combined. 
This paper proposes a new image fusion method in which the 
wavelet transformation is combined with the features of human 
vision system. This method can not only enhance the spatial 
detail ability of multi-spectrum image, maintenance the 
spectrum information, but also avoid strengthening the result 
ringing effect. 
are fully used in the imagery processing, the image quality will 
be greatly improved [7-8]. 
4.2 Fusion algorithm based on wavelet transformation and 
features of human vision system 
Fusion method theory is based on: Through the statistics of the 
value distribution, the sub-graph of source image A and B after 
wavelet transformation has such characteristics: 
(1 )The data change scope in the original map region is identical 
to that of the sub-graph corresponding region. 
(2) Regarding the different source images of identical goal or 
object, the data value of its low frequency image in 
corresponding region is the same or similar, but there is a 
remarkable difference in the high frequency sub-graph. The 
above-mentioned characteristic of the wavelet transformation 
has provided the theory basis for the choice of the effective 
fusion method. As for combining the wavelet transformation 
with the feature of human vision system, a new fusion method 
is brought up in this paper. 
(1) Carry on the geometry correction to the multi-spectrum 
image and the high resolution image respectively, use the 
geometry matching method based on the region, and then match 
the multi-spectrum image with the high resolution image. 
(2) Carry on a step wavelet resolving to the matched multi 
spectrum image and the high resolution image to obtain 
respectively the low frequency component and the high 
frequency detail component. 
(3) Carry on the uniformity method fusion to the two 
corresponding images of the high frequency and the low 
frequency sub-graph respectively, the processes are: 
(D Dissect the two images into some sizes for NxN blocks. 
Suppose Aj and Bj expresses the block of image A and B 
separately. 
(2) Calculate the uniformity measure of each block according to 
the formula which literature [9-10] provides. Suppose J(Ai) and 
J(Bi) are respectively the uniformity measures of Ai and Bi. 
(3) Compare the uniformity measure of the corresponding block 
of the two images, and obtain the fusion image ith block Fi 
F ; = 
4 
B, 
(A,+B,)/2 
J(A, )> J(B t ) + TH ^ 
J(A t )< J{B t )-TH 
otherwise 
Among them, TH is the threshold value parameter. 
4.1 Human eye vision system 
The image is to be looked at by humans, therefore the imagery 
processing should follow the features of human vision system. 
The vision is the result that images are left in human eyes. The 
complexity of visual processing has not have been understood 
and mastered by human beings at present. But people have 
discovered some visual phenomena, such as visual threshold, 
visual masking effect. If these features of human vision system 
(D In turn carry on the above operation to all image blocks, the 
new fusion image is obtained. 
(5) Carry on the wavelet inversion to the obtained high 
frequency and the low frequency sub-graph, and obtain the 
fused multi-spectrum image.