The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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(0 (j) 
Figure 3. Fusion of rural area images. (a)Original Pan image, 
(b)Original MS image, (c) Canny segmentation of Pan image, (d) 
Canny segmentation of MS image, (e) mean shift segmentation 
of Pan image, (f) mean shift segmentation of MS image, (g) 
Canny segmentation fused result, (i) Our proposed fused result 
As figure 3 shows, the test on rural area shows a similar result 
to the built-up area. The image in Figure 3(c) is over segmented. 
In Figure 3(d), some parts are over segmented and some are 
under segmented. The segmentation results shown in Figure 3(e) 
and (f) are more reasonable than those in Figure 3(d) and (e). 
The fused result based on Canny segmentation is blurred as can 
be seen from Figure 3(g) and (h). The sharpness and spectral 
reservation of the images fused by this new method are better. 
More detailed quantitative evaluation result is given below: 
image 
Entropy 
MI 
SF 
ERGAS 
1 
B 
6.6494 
1.2668 
8.4064 
12.0409 
G 
7.4298 
1.2648 
10.2679 
R 
7.4779 
1.2725 
10.9206 
Nir 
8.4651 
1.2521 
18.0159 
2 
B 
6.9650 
1.1759 
26.8664 
11.7954 
G 
7.4381 
1.1892 
30.5736 
R 
7.4379 
1.2147 
25.9148 
Nir 
8.5994 
1.2240 
37.7858 
Table 2. The quantitative results on rural area. (Image 1 from 
Canny segmentation fusion and image 2 from proposed fusion) 
6. CONCLUSIONS 
In this paper, a new technique for the fusion of high-resolution 
images has been described. In this technique, mean shift 
segmentation is adopted to extract the features from images. 
SSIM is used to measure the region similarity which has more 
physical meaning. The SSIM is then used to guide the decision 
making in the fusion process. Experimental evaluations have 
been conducted for built-up areas and rural areas. The results 
show this new technique performs better than the conventional 
technique with Canny detection operator. It has been found that, 
for high resolution image, the Canny detection tends to produce 
unstable segmentation, i.e. over segmentation in a sub-region 
and under segment in another region of the same images. On 
the other hand, mean shift segmentation is more reliable. The 
sharpness and spectral reservation of images fused by our 
proposed technique are better than those by conventional 
method with Canny segmentation. 
This conclusions made here are based on the limited tests. 
More comprehensive tests will be conducted in the future. 
ACKNOWLEDGEMENT 
This research was supported by State 973 project grant 
2006CB701304. 
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