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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B7. Beijing 2008 
(3) Design low-pass and high-pass decomposing filter that 
corresponds to the scale function p( x ) and wavelet 
function y/{x) respectively and design low-pass and high- 
pass reconstructing filter at the same time. 
(4) Decompose panchromatic image and synthesized 
hyperspectral color image on each layer by wavelet 
package decomposing algorithm and get the decomposed 
sub-images of both high and low frequency. 
(5) Fuse the panchromatic and hyperspectral sub-images and, 
in order to extend the application area of information 
composition, we propose an adaptive wavelet package 
fusion algorithm based on region features and the brief 
process is: 
(a) Assume the synthesized hyperspectral color image 
after wavelet package decomposing is A( X, y) and 
the panchromatic image is B(x,y), separate the 
colors of image A(x,y) to get three sub-images 
defined as Aj (jc,jp)( k = 1,2,3 and j is scale 
coefficient) and then apply histogram equalization to 
image B(x,y) and Aj(x,y). 
(b) Open a M x N window (usually 3x3) for 
image Aj ( X, and i?( X, jp), and in every image 
window, compute the square error D i , energy E i 
and information entropy S ( . 
(c) Calculate the pixel’s weigh on image Aj (.XjT’) and 
B(x,y) 
W' = a* Ei+b* D' + c* Sy, (8) 
Where a , b , c are weights of every feature and their 
default value is 1. 
(d) Get the fused sub-images by the following equation 
Fi(x,y) = (A)(x,y)-W a +W,,-B k (x,y))KW'+W b ) (9) 
Interpolate reconstructed image inversely by the above wavelet 
package reconstructing filters and get the final fusion image. 
3. HYPERSPECTRAL IMAGE FUSION EXPERIMENTS 
3.1 Experimental data 
The color image composition based on optimal bands and the 
fusion of high-resolution panchromatic image and hyperspectral 
image are two main parts of our experiment. The data used in 
our experiment was received in Oct 2003. It is PHI data of 
Shanghais Physics institution and is composed of 124 wave 
bands ranging from 400 to 850nm. The flight altitude is 2163 
meters and the latitude and longitude of the data is 31.18°- 
31.20° and 121.46°-121.51° respectively. In the original data, 
we selected the Yangpu bridge region (647x721 pixels) as our 
research interest. Additionally, for our fusion experiment and to 
obtain high-resolution color fusion image, we collected some 
high-resolution panchromatic images in the same area. 
3.2 Results and analysis 
In order to test the validity of our methods, we conducted a few 
experiments by the above PHI data. And the detailed fusion 
process is shown as follows. 
(1) Preprocess the hyperspectral data, and this step includes 
the correction of radiation, atmosphere, and geometry and 
so on [8]. 
(2) Preprocess the high-resolution images with geometry 
correction, select the optimal hyperspectral bands by the 
optimal index model and construct synthesized low- 
resolution color image. 
(3) Match hyperspectral and high-resolution images. This 
pixel matching step is quite important for image fusion and 
aims at eliminating the differences between images 
obtained by different sensors on the aspects of resolution, 
time, angle and confirming that every pixel of both images 
corresponds to the same spatial position. During the 
registering process, select some evenly distributed feature 
points on both images, and then register the images by 
these points through quadratic polynomial algorithm. After 
registering, resample the images through bilinear 
interpolation algorithm and output the images to end the 
image registering process. 
(4) Fuse the registered low-resolution color image and high- 
resolution panchromatic image by wavelet package 
algorithm described above. 
Practically, we can constraint the selection of the red, green and 
blue band of the synthesized image according to the wavelength 
range and the original images’ center to reduce the blindness 
and amount of computing. In our experiments, the red band was 
confined to the 43-46 th bands, the green band was confined to 
the 22-43 rd bands and the blue band was confined to the 1-21 st 
bands. The result of optimal bands was the 55 th , 33 rd and 10 th 
for the red, green and blue band respectively and the 
corresponding optimal index was 56.881. Figure 1 is 
synthesized low-resolution color image by optimal bands. It can 
be seen that the synthesized image greatly keeps the spectral 
characteristics of the original information and can provide 
favorable conditions for image understanding. Figure 2 and 3 
are the final fused images of the Yangpu bridge region, and it is 
clear that the fused images’ spatial resolution and clearness are 
greatly enhanced: automobiles on the bridge have more clear 
forms; marks in the garden’s center are more prominent; and 
the overall color information is satisfying.