The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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conventional region-based image fusion using Canny 
segmentation by both visual inspection and objective analysis. 
Four objective measures are used in this evaluation, i.e. 
Entropy, Mutual Information (MI), Spatial Frequency (SF) and 
Relative Dimensionless Global Error in Synthesis (ERGAS) 
(Eskicioglu, et al, 1995). 
Built-up area data sets 
Figure 2. Fusion of built-up 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 
A visual inspection of results shown in Figure 2 reveal that the 
Canny segmentation of Pan and MS images produces over 
segmented results and the resultant regions are too tinny to tell 
which one is useful. In contrast, the mean shift segmentation 
produces sounder results. The segmentation trends of Pan and 
MS images are nearly the same. The total number of regions in 
MS image is slightly more than that in Pan image with the 
mean shift segmentation but the relationship is reversed with 
Canny segmentation . It is clear that the image fused with mean 
sift segmentation is clearer than that with Canny segmentation. 
The spatial texture of images fused with Canny segmentation is 
disturbed due to over segmentation. 
image 
Entropy 
MI 
SF 
ERGAS 
1 
B 
6.8396 
1.2108 
20.2839 
12.9820 
G 
7.5488 
1.2141 
25.9663 
R 
7.8040 
1.2184 
29.3771 
Nir 
8.1422 
1.2126 
32.6054 
2 
B 
7.1512 
1.1520 
32.2683 
12.0265 
G 
7.5669 
1.1626 
39.6594 
R 
7.6649 
1.1792 
42.1544 
Nir 
7.9947 
1.1862 
41.3579 
Table 1. Quantitative results on built-up area. (Image 1 from 
Canny segmentation fusion and image 2 from proposed fusion) 
The objective evaluation is made to verify the visual analysis 
and the results are given in Table 1. From Table 1 we can see, 
that the Entropy in image 1 is larger than that in image 2 except 
for the blue band. The MI in image 1 is also a slightly larger 
than that in image 2. Of course, this doesn’t mean the quality of 
image 1 is better than of image 2 as the noise will also increase 
the information which can be verified by visual analysis. But 
the SF in image 2 is much higher than in image 1. This means 
the clarity in former image is higher than latter images which 
are consistent with the visual inspection. The smaller ERGAS 
implies error in all the bands is smaller. 
Rural area data sets