The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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resolution images with the high resolution images if in a step- 
by-step approach the input files and their spectral structures are 
controlled and prepared for the final merging. In this way, the 
results are much more satisfactory than the ones obtained 
through the Direct Merge method. For example, several linear 
and point features (wood fences, trees, etc.) that are difficult to 
distinguish in the Landsat images are visible in these merged 
images. 
2.3 Merge with Resampled Landsat Imagery 
In this method, at the first step multispectral Landsat 7 ETM 
imagery was resample to higher resolution imageries (20, 10, 5 
meters resolutions) step by step. At the second step, these three 
images were enhanced with filtering. Finally, panchromatic 
aerial imagery was merged with these this resampled Landsat 
imagery again with three different merging techniques. The 
merged images are shown in Figure 6 and also a part of imagery 
is zoomed in and shown in Figure 7. 
Figure 6. Merged imageries (a: Brovey Transform, b: IHS, c: 
Principal Component, d: Resampled Landsat multispectral) 
Figure 7. Merged imageries (a: Brovey Transform, b: IHS, c: 
Principal Component, d: Resampled Landsat multispectral) 
In this method, Brovey Transform gives the more satisfactory 
result by creating a good spectral reflectance. This method 
decreases the color spot effect in the direct merge method and 
creates a more homogenous merged imagery. 
3. CONCLUSIONS 
Merging methods for utilising both the high resolution 
panchromatic and the multispectral images in a combined 
manner is one way of improving the methods for many remote 
sensing applications such as change detection, classification, etc. 
The aim of the resolution merge is to achieve a maximal spatial 
detail augmentation and a minimal color distortion. 
The results of this study demonstrate that for the merge of aerial 
photographs and Landsat ETM imagery, Principal Component 
and Brovey Transform techniques achieve significantly better 
results than IHS. The main advantage of the images generated 
by the Principal Component and Brovey Transform techniques 
is that they have a close spatial characteristics with the aerial 
photograph. This could make the photo interpretation easier. 
Furthermore, these images have an aspect of softly colored 
aerial images, in which the color tones have been obtained from 
the Landsat ETM ones. The merged images are more similar to 
the Landsat ETM multispectral images, but with better spatial 
feature details derived from aerial photographs. 
Another conclusion is that merge with resampled Landsat 
imagery generates a better spectral merged imagery compared 
with the other methods. 
IHS technique doesn’t work well for imageries having such a 
big difference of spatial resolution. For this technique, merge 
with resolution merged Landsat imagery gives the best results. 
A lack of this technique is that since this technique creates only 
three spectral bands, it is not preferred for some remote sensing 
applications such as classification. 
Brovey Transform technique creates very sharp merged images 
but with some spectral problems. In direct merging, some color 
spots happen in the merged imagery. Merge with resampled 
Landsat imagery decreases this effect. 
In all of these techniques, Principal Component seems to work 
best for such imageries. It creates imagery with low spectral 
problems. Only the sharpness is less if compared with the 
Brovey transform. But this problem can be decreased by 
merging with resolution merged Landsat imagery. 
As a result of this study, it is advised that for the applications 
needing good spectral reflectancy; Principal Component 
technique and Merge with Resolution Merged Landsat Imagery 
method should be used and for the applications needing good 
spatial information; Brovey Transform technique and Merge 
with Resampled Landsat Imagery method should be used. 
REFERENCES 
Bretschneider, T., Kao, Odej.2004, Image fusion in remote 
sensing, Technical University of Clausthal, Germany, 
http ://www. ntu. edu. sg/home/astimo/Publications/Documents/O 
SEE2000.pdf 
Carvalho, J., Delgado-Garcia J., Soares, A., Caetano, H.,2006, 
Stochastic simulation with reference images for merging