The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
1173 
NIR 
- 
0.9854 
- 
- 
0.9843 
B 
0.6396 
0.7443 
0.8333 
0.8530 
0.8620 
UIQI ° 
R 
0.6870 
0.7856 
0.8556 
0.8586 
0.8710 
0.7607 
0.8186 
0.8713 
0.8712 
0.8910 
NIR 
- 
0.8535 
- 
- 
0.8676 
Table3. Quantitative analysis of different fusion methods 
5. CONCLUSION 
In ideal condition, a good image fusion method tries to 
generate the image which a sensor would obtain if it had the 
same spectral response of the original MS sensor but the spatial 
resolution of the PAN sensor. As a result, to preserve spatial 
details and minimize spectral distortion, the spectral 
characteristics of sensors have to be taken into account. The 
proposed method is based on the generalized fast IHS fusion 
framework and two improvements are proposed by considering 
sensor spectral response. As shown from the different fusion 
experiments, the proposed method has a superior 
comprehensive performance and performs better than other IHS 
fusion methods regarding both spectral and spatial quality. It is 
suitable for various satellite images and extends traditional 
three-order transformations to an arbitrary order. 
Figure3. Part of Landsat7 ETM+ MS image and fusion result: RGB (743) combination, (a) Original MS image; (b) Fused by IHS 
method; (c) Fused by proposed method 
Figure4. Part of EO-1 ALI MS image and fusion result: RGB (432) combination, (a) Original MS image; (b) Fused by IHS method; 
(c) Fused by proposed method 
express sincere gratitude to Doctor J. L. Li for some very 
helpful comments. 
This work was supported by the Geospatial Information Science 
and Technology program (IRT 0438). The author would like to 
thank the United States Geological Survey and the Space 
Imaging Company for providing images for research and 
ACKNOWLEDGMENTS