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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
registered. As for resampling, the Ikonos multispectral images 
are resampled from 4m to 2.8m pixel size, which is 2? times 
0.7m of the QuickBird panchromatic image resolution and is 
the closest number to 4m. In this way, the necessary resampling 
is limited to the minimum and the quality of the original image 
is best retained in this process. After this preprocessing, 
procedures outlined in Figure 2 are followed to conduct the 
fusion process. The outcome of this process is four 
multispectral images with a spatial resolution of 0.7m. 
  
  
  
   
  
  
   
  
Original Multispectral Fused Image 
  
  
  
Image (Ikonos) 
Figure 4. Fusion of QuickBird pan and Ikonos multispectral 
images 
  
5.3. Visual and Quantitative Evaluation 
5.3.1 Visual Evaluation 
For visual evaluation, two approaches are used. First, the 
proposed fusion algorithm is evaluated in terms of spatial and 
spectral improvements. It is clearly seen from the pictures in 
Figure 3 and 4 that the spatial resolutions of the images after 
the fusion are improved. In the original QuickBird and Ikonos 
images, it is very difficult to discern some physical features like 
small buildings. For example, in both multispectral images 
there are some circular objects that are very difficult to perceive 
whether they are buildings or not. In Ikonos, it becomes even 
more difficult, nearly impossible, to see these plants (the blue 
circled area). 
However, in both fused images, it becomes clear that there are 
some circular man-made features, which are most likely silos 
that farmers use to store their crops. Also, there is a small road 
or water way that can be apparently seen in the fused images 
(blue oval), which are not perceivable in the original Ikonos 
image. The fused images also keep the original colors that 
means that the spectral content of the images are carried to the 
fused ones. Therefore, the fused images will significantly 
improve the image classification results. 
Secondly, as a comparison, the same images are fused also 
using PCA, Brovey and Multiplicative image fusion techniques 
using Erdas Imagine 8.6. Figure 5 below presents the fusion 
results of QuickBird Pan and Multispectral images. It is seen 
that these fusion methods also improve the spatial resolution. 
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But, the colors of the features in the fused images are changed. 
This color distortion effect is the largest in Brovey method. 
Among these three methods, multiplicative transformation 
gives the best result in terms of color conservation. However, 
wavelet transform approach is superior to these three results, as 
the colors of the features in original multispectral images are 
nearly the same in the fused image. 
Finally, the images are fused using DB wavelet. As seen from 
the Figure 6, the DB wavelet gives a better spatial resolution 
when compared to the results from Haar wavelet. This implies 
that the selection of different wavelets may affect the fusion 
results. In this paper, for its simplicity, fusion results of Haar 
wavelet are used for algorithmic description and the 
comparisons with the results of other fusion methods in the 
visual evaluation part. All correlation coefficients used in 
quantitative evaluation part are also calculated using the fused 
images with Haar wavelet. 
  
   
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Multiplicative Method Multiplicative Method 
  
Figure 5. Fusion of QuickBird pan and multispectral images 
using PCA, Brovey and Multiplicative fusion methods. 
    
    
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Figure 6. Fusion using Haar (left) and DB 2.2 (right) wavelets.