The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
1226 
Figure 1. Synthesized hyperspectral image 
keeps the original hyperspectral data’s spectral characteristic 
and enhances the degree of image’s transparency and sharpness. 
4. CONCLUSIONS 
Image fusion for hyperspectral data and high-resolution data is 
an emerging technology with the development of remote 
sensing imaging technology. It has tremendous application 
future on such areas as city planning, environment monitoring, 
land utilization, military reconnaissance and so on. However, 
most of the current researches and attempts are just tentative. 
To convert these theories and techniques into generalized rules 
and to apply them to remote sensing area successfully remains a 
long way. In this paper, after studying some related algorithms 
in the image fusion area, we proposed a wavelet package fusion 
algorithm for hyperspectral image based on optimal index band 
selection principle and experiments bring us to make the 
conclusion that this approach can generate hyperspectral fused 
images effectively and robustly. 
Figure 2. fusion image Figure 3. part of the fusion image 
Information entropy is an effective index to measure the degree 
of image’s information; mean gradient can reflect the contrast, 
texture characteristic and clearness of images; correlation 
coefficients can reflect the similarity between the fused image 
and the original hyperspectral image. Thus, we choose 
information entropy, mean gradient and correlation coefficients 
and other measures to conduct quantitative analysis and the 
results in shown in table 1. 
Fusion 
Algorithm 
Information 
entropy 
Standard 
error 
Mean 
Correlation 
coefficient 
s 
Original 
Panchromati 
c Image 
7.650 
57.598 
9.529 
Original 
hyperspectral 
image 
10.756 
29.307 
1.777 
Fusion image 
13.095 
57.78 
9.566 
0.838 
ACKNOWLEDGEMENTS 
This work described here is partially supported by the grants 
from the Open Research Fund of State Key Laboratory of 
Information Engineering in Surveying, Mapping and Remote 
Sensing, the Key Laboratory of Remote Sensing, Science and 
the National Research 863 Program of China(Grant No. 
2007AA12Z147). 
4.1 References 
[1] Zhang Bing, 1997. Hyperspectral data mining supported by 
temporal and spatial information, Ph.D. dissertation, Institute of 
Remote Sensing Applications of Chinese academy of sciences, 
Beijing, pp. 80-88. 
[2] Jong-Hyun Park, Ryutaro Tateishi a, Ketut Wikantika,2000, 
Multisensor data fusion using multiresolusion analysis. ISPRS, 
pp. 987-1002. 
[3] Zhang Jun-Ping, Zhang Ye,2002, Hyperspectral image 
multi-solution fusion based on local information entropy, 
Chinese Journal of Electronics, 11(2), pp. 163-166. 
[4] Wilson Terry A, Regers Steven K, Matthew Kabrisky,1988, 
Perceptual-based image fusion for hypetspectral data, IEEE 
Trans, On G.R.S, 35(4), pp. 1007-1017. 
[5] Zhang Jun-Ping, Zhang Ye,2004, Hyperspectral image 
classification based on multiple features during multiresolution 
fusion, J. Infrared Millim Waves, 23(4), pp. 345-348. 
[6] Zhu Shu-Long, Geng Ze-Xun,2000, Wavelet transforms for 
remote sensing image processing, Beijing, pp. 24-25. 
Table 1. Quantitative analysis results of fused image 
From table 1, it can be seen that the information entropy, mean 
gradient and correlation coefficients of the fused image by 
wavelet package fusion method based on optimal index 
principle are greater than the original image. In other words, it 
means that the fused image has richer information, greatly