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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
image 
band 
mean 
standard 
deviation 
entropy 
Correlation 
coefficient 
with XS 
image 
average 
gradient 
Correlation 
coefficient 
with Pan 
image 
XS image 
5 
75.183 
21.675 
6.0952 
1 
4.0176 
0.6541 
4 
71.941 
15.599 
6.0627 
1 
2.8171 
0.7284 
3 
66.227 
18.682 
6.0598 
1 
3.2773 
0.1661 
HPF fused 
5 
75.113 
22.141 
6.3509 
0.9455 
6.6193 
0.7214 
image 
4 
71.871 
15.884 
6.2881 
0.9636 
6.5239 
0.7956 
3 
66.157 
21.041 
6.2971 
0.9843 
5.6477 
0.2985 
SFIM fused 
5 
75.161 
22.966 
6.291 
0.9548 
8.0911 
0.7365 
image 
4 
71.810 
17.516 
6.2199 
0.9603 
7.3351 
0.8139 
3 
65.971 
19.383 
6.2274 
0.9537 
7.2359 
0.3127 
ML fused 
5 
100.228 
23.161 
6.2391 
0.9277 
8.1029 
0.8883 
image 
4 
98.153 
19.267 
6.2595 
0.9433 
7.6337 
0.9140 
3 
93.150 
19.167 
6.0762 
0.8425 
7.952 
0.6674 
Brovery 
5 
47.925 
15.982 
5.6371 
0.7847 
9.9214 
0.9543 
fused image 
4 
47.965 
21.041 
6.2393 
0.9732 
9.1894 
0.6699 
3 
40.908 
9.767 
5.1429 
0.7844 
9.266 
0.7208 
IHS fused 
5 
73.574 
14.842 
5.9055 
0.8231 
6.5412 
0.8970 
image 
4 
71.761 
22.728 
6.4812 
0.8786 
6.8252 
0.7000 
3 
64.028 
21.48 
5.8278 
0.8157 
6.5109 
0.4894 
PCA fused 
5 
136.353 
28.614 
6.9469 
0.6441 
10.3735 
0.9613 
image 
4 
134.02 
13.613 
6.9428 
0.9546 
9.396 
0.7738 
3 
137.837 
17.966 
5.9238 
0.2372 
9.6198 
0.9734 
Table 3. Table of evaluate parameters 
4.1 Parameters Statistics of Fused Image 
The original multi-spectral images using XS to replace, and 
panchromatic images with PAN replaced, evaluate parameters 
are shown in the table3: 
From the parameters of table 3, we can see that: 
(1) All fusion method in accordance with the definition in 
descending order, the order is: PCA>Brovery>ML> 
SFIM>MIHS>IHS>HPF; 
(2) All fusion method in accordance with the Spectra maintains 
degrees in descending order, the order is: HPF>SFIM> 
Brovery>MIHS>ML>IHS>PCA; 3 
(3) All fusion method in accordance with the entropy in 
descending order, the order is: PCA>MIHS>HPF>IHS>SFIM> 
ML>Brovery. 
4.2 Feature Identification Accuracy of Fused Image 
Different fusion methods have different impacts on image. 
Image Recognition is the application of spectral characteristics 
and structural characteristics of different features to identify 
information; therefore spectra and texture information on the 
objectives of the interpretation are important significance [10] . 
In order to verify the influence of various fusion methods on the 
classification accuracy, in this paper, the image data of different 
experiments using the same processes to deal with unsupervised 
classification; and make classification accuracy 
test, select high precision fused image to make supervised 
classification. 
4.2.1 Research Methods: Make classification with maximum 
likelihood classification; using random method to select 256 
ground inspection points, make accuracy test for thematic maps 
of XS image and fused image, obtain total accuracy and Kappa 
index. 
4.2.2 Accuracy Test of Unsupervised Classification 
From the comparative data table 4, we find that: PCA fusion 
image, in addition to other fusion image classification accuracy 
are significantly higher than those without fused image 
classification accuracy, the reason maybe that: PCA fused 
image has the worst spectrum distortion, and it leads to the 
lower classification accuracy. Descending order of the 
classification accuracy is: SFIM> HPF >ML>Brovery > 
XS>IHS>PCA. 
type 
XS 
image 
HPF fused 
image 
ML fused 
image 
Brovery fused 
image 
PCA fused 
image 
SFIM fused 
image 
IHS fused 
image 
Total 
77.34% 
81.25% 
80.47% 
78.52% 
67.97% 
84.38% 
76.95% 
accuracy 
Kappa index 
0.6799 
0.7468 
0.7298 
0.6809 
0.5271 
0.7810 
0.6454 
Table 4. Comparative data of image unsupervised classification accuracy