The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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4. EXPERIMENTAL RESULTS AND COMPARISONS 
4.1 Synthetic Datasets and Real MS-PAN Datasets 
The main aim of this research is to determine the efficiency of 
new algorithm based on ICA for merging images with a 
particular resolution ratio. Due to the difficulties in obtaining 
adequate imagery with particular ration, Yocky’s approach 
(Yocky, 1996)is employed to synthesize some MS-PAN 
datasets with particular ratio. In this approach, a Landsat TM 
test image was available in the three bands,i.e., B1 (green), B2 
(red), and B3 (near infrared). The image was used to synthesize 
a perfectly overlapped panchromatic band at 20 m, which is 
shown in fig2. 
4.2 The Quality Analysis of the Fusion Image 
We have adopted some quantification metrics to evaluate the 
fusion quality, including entropy, mean, and standard 
deviation, Average gradient. Among these metrics, entropy 
explores the information changes, and an image has more 
information when the entropy is bigger. And some other metrics, 
such as mean, employed to evaluate the aberrance of the 
spectral information. The mean calculates the degree of the 
spectral information change. In our research, different 
decomposition levels for wavelet have been tested. Limited by 
space, only the result for true data set is show in Fig 4. 
For visual analysis, we could find that our method can enhance 
the image spatial resolution to a certain degree, which will 
benefit those applications which are demanding strictly on the 
a. original MS 
bands( 128x128) 
b. 2 level 
c.3 level d.4 level 
Fig 4 he fusion result with different decomposition levels for 
true data size(512><512) 
details of an image, such as image interpretation, special 
cartography, and photogrammetric survey, etc. With 
decomposition level increasing, more panchromatic band 
information is injected into three multispectral bands and gray 
levels of images seems no demonstrate change, which means 
that our method may be not sensitive to wavelet decomposition 
level.The synthesized data has shown the same trend. So when 
we consider the computation efficiency, the less decomposition 
level such as 2 or 3 is preferable. We have also adopted some 
quantification metrics to evaluate the fusion quality. The 
statistical data for true data is shown in table 1. It can be 
included from tables: the information in both of the two datasets 
is increased in the case of the injection of the 
information .Because the high frequency information in the 
multispectral bands is substituted by the corresponding parts in 
the panchromatic band. However when decomposition level 
increased, the result had no demonstrable change. It means that 
our method is not sensitive to decomposition level as tradition 
MRA based method. So we proposed a useful fusing algorithm. 
Metrics 
Band 
Original 
spectral 
2 
3 
4 
R 
bands 
113.59 
113.23 
113.23 
113.20 
Mean 
G 
100.23 
99.87 
99.87 
99.84 
B 
93.85 
93.42 
93.42 
93.45 
Standard 
deviation 
R 
73.50 
74.10 
74.09 
73.78 
G 
70.51 
73.27 
73.28 
72.49 
B 
70.65 
69.44 
69.44 
69.71 
Average 
gradient 
R 
14.57 
21.97 
21.97 
21.71 
G 
14.66 
20.15 
20.15 
17.57 
B 
14.39 
16.21 
16.21 
17.38 
R 
4.97 
7.90 
7.90 
7.9 
Entropy 
G 
4.94 
7.83 
7.83 
7.85 
B 
4.85 
7.80 
7.80 
7.80 
Table. 1 statistical data for true MS-PAN dataset 
5. CONCLUSION AND PROSPECTS 
A new multispectral and panchromatic band merging method is 
provided by combining ICA transform with discrete wavelet. 
The experiment result shows that the method can improve the 
spatial information of original spectral bands effectively. But 
spectral distortion is still a problem in fusion result. In the 
future, our work is focused on establishing a more flexible 
fusion rule for information displacement. 
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