International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
DN (x, y) = DN, (x, y) + (DN, (x, y) - DN, (>, y)) 
Therefore, 
DN,(x, v) = DN,(x, y) (6) 
From the equation (6), the average value for a group of 4x4 
sub-pixels in the new image is equal to their correspondent 
pixel value in the IRMSS image. Obviously, the new image has 
the same spectral energy as the IRMSS image within one 
original pixel of the IRMSS image. So that, the data fusion 
image produced by this method can keep the spectral energ 
balance and can preserve the fidelity to the lower resolution 
image spectral properties. The new method can also be called as 
Spectral Energy Balance method. 
4. APPLICATION 
Based on the introduction as above, data fusion methods at the 
present can be divided into two methods: spectral component 
substitution techniques and spatial domain techniques. The 
former replaces a spectral component of the multi-spectral 
images by an adjusted high resolution image. The latter adds 
high resolution information from the high resolution image to 
all the low resolution spectral bands. Obviously, the spatial 
domain techniques will not keep the spectral energy balance 
and the spectral fidelity to the lower resolution image will be 
destroyed because of adding a variable value to every pixel of 
the low resolution image. According to hue theory, any colour 
is quantitatively defined in terms of three variables: hue, 
intensity and saturation, which give a numerical description of 
the spectral range, brightness and purity of a colour. These 
three variables are independent of each other (Qi, 1996). HIS 
method for data fusion is to replace an intensity of the three 
Band colour composite image with a high resolution image. 
Based on the hue theory, intensity is independent of hue and 
saturation to some extent, and the hue and saturation will not be 
affected by such substitution in HIS data fusion. PCA method is 
to replace the first principal spectral component in principal 
component analysis of the multi-spectral images with a high 
resolution image. Because the first principal component takes 
the most information in principal component analysis, and it is 
dependent of the solar incident radiance and the spectral 
properties of the surface materials, any other band image cannot 
represent the first principal component either in information 
content or spectral properties. Therefore, the HIS method is 
better than the PCA method in preserving the spectral fidelity. 
Preserving spectral fidelity method also belongs to the spectral 
component substitution techniques and it replaces a low 
resolution band with an adjusted high resolution image, i.e., 
every pixel brightness value in the high resolution image is 
adjusted by its co-registered pixel in the low resolution image. 
Therefore, the fusion results produced by HIS and PSF methods 
will be compared, mainly in preserving the spectral fidelity. 
CBERS images in Guizhou Province, China, taken on July 18, 
2000 are used for test. Image data is a 2 grade image data, 
supplied by China Centre for Resources Satellite Data and 
Application. CCD B4 image was selected as a higher resolution 
image and IIMSS B6,B7,B8 were selected as lower resolution 
images. Original image size is 400x200 pixels for CCD image 
and 100x50 pixels for IRMSS images. 
Three gray scale images based on CBERS CCD B4, IRMSS B7 
and their data fusion image are shown if Fig.1: image marked A 
is CCD B4; image B is IRMSS B7; image C is data fusion 
image of B4 and B7 by PSF method. Image B and C show 
clearly that the fusion images have more spatial details than 
IRMSS B7, and have a similar spectral information to B7; From 
image A and C, the fusion image contains the similar spatial 
details in B4 image, but image brightness distribution in image 
B is definitely different from image C. 
  
Figure 1. Gray scale images based on CBERS CCD B4, IRMSS 
B7 and their data fusion image: image A is B4; image B is B7; 
image C is data fusion image of B4 and B7 by PSF method 
Three Band colour composite image can more clearly show the 
difference between the original image and the data fusion image. 
Image marked A in Fig. 2 is a three band colour composite 
image of B7, B8, B6; image B is a data fusion image derived 
from B7.B8,B6 and B4 by HIS method; image C is a dal 
fusion image by PSF method. Compared with image A. both 
image B and C had more spatial details, such as some small 
water system, which indicates that the data fusion method can 
be used to enhance image spatial details and improve image 
spatial resolution for lower resolution image; image C had the 
similar colour to image A, while image B is quite different from 
image A in colour. Which confirms that PSF method can be 
used for preserving the spectral fidelity of the lower resolution 
image in data fusion processing, but HIS method cannot 
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