Full text: Proceedings, XXth congress (Part 7)

  
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
Image to the left side is the quality matrix that is made up of 
the correlation coefficients calculated for QuickBird 
panchromatic band and the original multispectral band-1. The 
image to the right shows the corresponding quality matrix for 
the QuickBird panchromatic band and the fused multispectral 
band-1. By doing this, the responses of the some geographic or 
physical features to the fusion algorithm can be detected. For 
example the image on the left side is darker than the one on the 
right side. This implies that the image on the right side is made 
up of larger correlation coefficients. This result is proven from 
the Table 2. The over all correlation coefficient for original 
multispectral band-1 and the fused band-l is 0.69 and 0.77 
respectively. 
6. CONCLUSIONS 
It is demonstrated that, when different sensors are used, the 
image co-registration becomes important. Even if two input 
images have the same projection and datum, they are generated 
independently with different processing steps, sensor models, 
trajectory data and ground truth. It is observed that the same 
ground features in the Ikonos and QuickBird images have 
apparent mis-registration. For this reason, while preparing the 
images for the fusion process, careful attention must be given 
to make sure that the same pixels in the two images represent 
the same geographic position in the field. 
For different sensors, the temporal difference between the 
acquisitions of the two images also causes some problems. If a 
feature in one of the images is not exist anymore, or changed 
partially, this will result in poor quality in the fused image. 
In general, a good fusion approach should retain the maximum 
spatial and spectral information from the original images and 
should not damage the internal relationship among the original 
bands. Based on these three criteria, correlation coefficients are 
used to quantitatively evaluate the image fusion results. The 
higher correlation coefficients between the panchromatic image 
and the fused image imply the improvement in spatial content 
when compared to the correlation coefficient calculated for 
panchromatic and original multispectral images. Likewise, a 
fused image should have high correlation to the corresponding 
original multispectral image to retain spectral information. In 
addition, the fused multispectral images should preserve the 
same correlation properties as the ones of the original 
multispectral images. Therefore, their difference needs to be 
small. Fusion quality can also be evaluated locally, where 
correlation coefficients are calculated within a neighborhood of 
a pixel. In this way, the proposed quality measure can help 
understand the responses of different geographic features to the 
fusion algorithm. It is shown that the fused image have over 0.9 
correlation except band 1 (blue band) with the original 
multispectral images, and 0.7 correlation with the panchromatic 
image. It is the highest comparing the tested exiting fusion 
methods: PCA, Brovey and multiplicative. This reflects a good 
retaining of both spatial and spectral information during the 
fusion process. 
This study is a successful experience with the wavelet 
transform based fusion approach. It is shown that proposed 
wavelet transform approach improves the spatial resolution of a 
multispectral image while it also preserves much portion of the 
spectral component of the image. Some features that can not be 
perceived in the original multispectral images are discernable 
in the fused ones. By properly designing the rules in combining 
the wavelet transform coefficients, color distortion can be 
minimized. Fusion results preserve the same color appearance 
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as the original multispectral images, even when images 
collected by different sensors are involved. 
Finally, different wavelets tend to yield different fusion quality. 
This is observed when comparing the fusion results obtained 
from Haar and Daubechies wavelets. It is shown Haar wavelet 
may cause the effect of squared feature boundary, where 
Daubechies wavelet presents a smooth and natural transition. 
This topic along with further formulation of fusion quality will 
be our future effort of study. 
7. REFERENCES 
Chavez, PS. Ir, Sides, S.C. and Anderson, LA. 199]. 
Comparison of three different methods to merge 
multiresolution and multispectral data: Landsat TM and SPOT 
panchromatic, Photogrammetric Engineering and Remote 
Sensing, vol.57 (3), pp.295-303. 
Chibani, Y., Houacine, A., 2002. The joint use of HIS 
transform and redundant wavelet decomposition for using 
multispectral and panchromatic images. /nt. J. Remote Sensing, 
Vol.23, No.18, pp. 3821-3833. 
Du, Y., Vachon, P.W., van der Sanden, J.J., 2003. Satellite 
image fusion with multiscale wavelet analysis for marine 
applications: preserving spatial information and minimizing 
artifacts (PSIMA). Can. J. Remote Sensing, Vol. 29, No. |, pp. 
14-23. 
Hill, P., Canagarajah, N., Bull, D., 2002. Image fusion using 
complex wavelets. Proceedings of the 13th British Machine 
Vision Conference, University of Cardiff 2-5 September 2002. 
Li, H., 1994. Multi-sensor image fusion using the wavelet 
transform. [CIP-94., IEEE International Conference, Vol. 
1, 13-16 pp. 51-55. 
Misiti, M., 2002. Wavelet toolbox for use with matlab. 
Wavelet toolbox user’s guide by The MathWorks Inc. 
http://www.mathworks.com/access/helpdesk/help/pdf. doc/wavl 
et/wavelet. ug.pdf. (accessed 04/15/2004) 
Nikolov, S., Hill, P., Bull, D., Canagarajah, N., 2001. Wavelets 
for image fusion. In: Wavelets in Signal and Image Analysis. 
Kluwer, Netherlands, pp.213-239. Editors: Petrosian, A.A. 
and Meyer, F.G. 
Pohl, C., van Genderen, J. L., 1998. Multisensor image fusion 
in remote sensing: conceps, methods and applications. /nt. J. 
Remote Sensing, vol.19, No.5, pp. 823-854. 
Ranchin, T., Aiazzi, B., Alparone, L., Baronti, S., and Wald, L., 
2003, Image fusion—the ARSIS concept and some successful 
implementation schemes. /SPRS Journal of Photogrammetry 
and Remote Sensing 58, Issues 1-2, June, pp. 4 — 18. 
Zhou, J., Civco, D.L. and Silander, J.A, 1998, A wavelet 
transform method to merge Landsat TM and SPOT 
panchromatic data. /nt. J. Remote Sensing, vol.19, No.4, pp. 
743-757. 
 
	        
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