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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B1. Istanbul 2004 
Raw materials are integrated and managed on CManage class 
and CFusion class is responsible for image fusion processing. 
Each algorithm for image fusion processing is configured to 
components. 
4.1 Development of Relevant Components 
Components developed related to this study were classified 
into total five categories according to component functions. 
Classified categories are data input, image filtering, image 
analysis, image fusion and output component. The details are 
explained below. 
1 - Data Input Component 
Image Loading Library(Tif, Jpg, Raw): 
CSatelmage Class Allocation 
2 - Filtering Component 
Image Improvement Component : CFltImage Class 
3 - Image Analysis Component 
Analysis of Fusion Images : Normalization, 
Equalization, Histogram Component 
4 - Image Fusion Component 
Integrated Fusion Component Management 
Class 
Analysis of Fusion Images: FusionImage Class 
Optimization of Memory Use : CRetlmage Class 
5 - Output component 
Output Component : CFusionlmage 
CFusion 
4.2 Developed Program Format 
Program window in Figure 5 shows multi-band image input 
window. Overall program menus comprise of Enter and Save 
File, Image, Layer, Select, Fusion, Tools, Windows and Helps. 
Submenu on the left side consists of Fusion Method, Pass Step, 
Rough Images on Pass and Display Window of Image Size. 
© CIO 
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Fusion Method | 
Pass Depth 
Edge Process 
— = 
WH APass 18 p 
       
  
  
  
  
Figure5 Multi-band display of fusion program. 
5. APPLICATION TEST AND ANALYSIS 
5.1 Application Test 
This study used multi-band images from LANDSAT taken 
around the Hangang river in Seoul on April 4, 1995 and images 
from KOMSAT-1 taken on the same area at 9 minutes to 2 on 
March 1, 2000. The same points on each image were manually 
selected and finally selected using Brightness Correlation. 
Geometric transformation formula between two images was 
defined using Affine Transformation and images were 
rearranged by bilinear interpolation method. Next, two images 
were created for image fusion and IHS and Wavelet fusion 
methods were applied to those two images. HPF fusion images 
had relatively inferior resolutions so that they were excluded 
from analysis. 
5.1.1 IHS Method 
Low resolution images from LANDSAT were prepared by 
band and were transformed to Intensity(l), Hue(H) and 
Saturation(S). In Figure 6, 1, 2 and 3 means the upper limit of I, 
H and S, respectively. images on the upper limit 4 are fusion 
images. Dominant appearance of yellow was phenomenon 
caused by fusion because the angle of Hue was expressed only 
to | in an angle unit. 
Intensity images were reversely transformed by RGB and the 
results were shown in Figure 8. R, G and B mean |, 3 and 4 
quadrant, respectively. These images were fused using 
intensifies of high resolution images. At this point, images were 
replaced by pixel values corresponding to each image size. 
Consequently, for images in 1:4 size, the intensify of one 
pixel of low resolution image was replaced by four pixel values. 
On the result image on the left side, slightly light colors were 
appeared on mountain areas and for areas with higher 
brightness, blue or sky blue color was shown. In terms of 
calculation, we could get values exceeding 0-255. These values 
were obtained because relevant values were lost during linear 
stretching for images to adjust these values. The final fusion 
images were presented on the left side of Figure 6. 
  
     
Le me 
Figure6 RGB reverse transformed image and fused image. 
    
5.1.2 Wavelet Method 
Wavelet transformation was applied to low resolution 
images from LANDSAT as well as high resolution images from 
KOMPSAT. As shown in Figure 2, these processes passed 
through low pass filter and high pass filter. The images in the 
size of 512*512 were reduced to 256*256—128*128—64*64 
—32*32. Therefore, we had limit that image sizes should be 2”. 
In general as Wavelet transformation was processed in 
multiple steps, spectral resolution of low resolution images and 
spatial resolution of high resolution images could be fused 
much better. In accordance with analysis using a developed 
system, when image transformation exceeded appropriate 
image compression rate in Wavelet transformation, images 
were transformed and so influenced on image fusion. Images in 
Figure 7 described that image resolutions were increased as 
result passes of each band according to the number of each pass 
were repeated. It was found that four Wavelet transformation 
steps created the best image fusion in accordance with system 
analysis. From fifth step, images were transformed so that 
image fusion was not properly carried out. 
This wavelet transformation was applied from step 1 to step 
4 as passing through low resolution image and high resolution 
image by step. When rough images and precise images were