International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B5. Istanbul 2004
Origina Fusion Methods
Quality
Axes” de IHS PCA âtrous
R 9079 21.230. 21149 22.240
Gradient — G 7517 701.500] 20866 21.975
B 7.804 21505 20925 22.096
R 4912 7810 7044 797357
Entropy G 4528 “9800 7845 4849
B 4933 7099897 073994. 9 339
JE : 6788 "4.00 11927 17845
Table 4. Comparisons of joint entropy and different methods in
application to quality assessment of fused images
Compared with the original multispectral image, the entropy,
gradient and joint entropy of any fusion method are much
higher, which indicates that the spatial quality of fused images
has improved greatly in details and local lucidity. According to
Table 4, joint entropy can get the same results as other quality
measures, where the superior is wavelet fusion method, the
inferior are IHS and PCA. However entropy and gradient can
only be used to calculate the single grey image. On one hand,
its not convenient to compare the effects of colour-fused
images, on the other hand, there are redundancies among the
three channels in fused images and both of them cannot
evaluate the whole spatial information precisely. As a criterion
of the whole information content, joint entropy solves this
problem efficiently.
S. CONCLUSIONS
In order to reduce the space-time complexity of joint entropy,
an alternative solution based on improved index data structure
has been developed, and this solution can be extended to
calculate the multidimensional joint entropy. The experiments
were conducted to put this new solution into the applications to
optimum band selection and quality assessment of fused images.
Some available statistical techniques of these applications are
also used to compare with joint entropy. It is showed that the
results of joint entropy are consistent with those of other
methods or even better. In application to optimum band
selection, joint entropy can obtain good or better triplets
compared with other methods; while used to evaluate the
quality of remote sensing image data, joint entropy as a
criterion is more apt to assess the spatial details in fused images
and can get more exact results than other methods. All the
experiments indicated that the improved algorithm of joint
entropy could be used as an efficient image analysis tool in
remote sensing.
ACKNOWLEDGEMENTS
The author would like to thank Mr. Li Junli in School of
Remote Sensing Information Engineering, Wuhan University
for valuable comments and suggestion.
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