The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
calibrated, the platform pose was corrected only. More details 
are not discussed in this article. 
Figure 2 Data preprocessing flow 
2.2 Preprocessing For fusion 
Special filter are designed for the fusion .It is a filter called 
neighborhood- cluster vector filter-NCVF (Ma Yanhua, etc, 
2006) which base the clustering of a pixel’s neighborhood area 
to reduce the scattered minor spectrum. It can keep the spectral 
information unchanged when erasing noise and small areas of 
odd spectral, at the same time it can sharpen the edge of the 
images in some extension. A practicable algorithm that 
similar with the vector median filter (VNF) was designed to 
realize the filter. 
Edge detection is used to segment the High special image for 
pure pixel spectral diffusing, and used to detect pure pixel for 
hyper-spectral, to be introduced more in this article .The hyper 
spectral edge detecting usually adopt a simple method: 
principal component transformation and then use ordinary Edge 
detection algorithm, such as sobel operator,etc. 
2.3 register strategy 
To meet different applications, all kinds of image fusion 
algorithms are realized ,include fusion algorithms that are now 
generally used, such as the PCA, high pass filter , wavelet 
transformation algorithm, etc, but most of them are depended 
on register precision(figure 3). The difference of GPS of the 
images that used in this article is very large, the low special 
resolution hyperspectral image has to be blurred, the spectral 
are blurred too, so the fusion result must be appended the effect 
of image re-sampling, just as showed in figure 4. 
In figure 4,(a)shows a hyperspectral image of AMIRS after 
register; (b)is the high special image; (c)and(d) is the results of 
high pass filter fusion and PCA algorithm. The result shows 
that with such a resolution difference, the spectral after register 
has been blurred too bad to cite. 
Figure 3 general image fusion flows 
Figure 4 the low resolution image is blurred after register 
At the same time, big resolution difference and short of precise 
geometry correction of the experiment images 
Depresses the register precision, so a register strategy is put 
forward. 
Rough registration means getting the spatial relationship of the 
pixels in two images, but the hyperspectral image are not 
re-sampled according to high special resolution image. General 
registration algorithms can be used. 
Because the two kind images are captured at the same time and 
the two images are calibrated ,and the spectralwave are known 
and overlapped ,so the luminance in corresponding bands(the 
sum of several bands of hyperspectral image with one band of 
RGB image) are high correlation, the registration between the 
super pixel and the high special then with the brightness 
correlation, you can judge which block(in spatial segment 
image ) a spectral belong to by moving the spectral image pixel 
in a initialized range around the rough registered position the 
RGB image . 
By the strategy, the blur of re-sample ,the problem of register 
precision are resolved. 
3. FUSION 
In this article the target of fusion is to get classify information 
on spectral and spatial distributing information on 
corresponding high spatial images. 
3.1 workflow 
A workflow to carry out the target is sesigned as below. Showed 
in figure 5. 
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