27 
Results 
y 
n 
Combination 
MSD(Level2) 
SD 
EN 
CE 
MI 
UI 
WA-WBA+ 
NG+ 
AMO+ 
RBV 
LPT 
61.2172 
7.5279 
1.9536 
2.5195 
0.6933 
Morph 
61.7087 
6.8094 
2.4341 
5.7131 
0.7192 
DWT 
60.2194 
7.6022 
1.7723 
2.2389 
0.6911 
SiDWT 
61.5087 
7.1942 
2.4854 
5.1215 
0.7235 
Combination 
MSD(Level3) 
SD 
EN 
CE 
MI 
UI 
WA-WBA+ 
NG+ 
AMO+ 
RBV 
LPT 
60.4544 
7.5507 
1.9186 
2.2094 
0.6927 
Morph 
61.7250 
6.8123 
2.4279 
5.7029 
0.7191 
DWT 
59.3618 
7.5947 
1.8653 
1.9139 
0.6785 
SiDWT 
61.5075 
7.1966 
2.4801 
5.1154 
0.7235 
Combination 
MSD(Level4) 
SD 
EN 
CE 
MI 
UI 
WA-WBA+ 
NG+ 
AMO+ 
RBV 
LPT 
59.7312 
7.5428 
1.8338 
2.0376 
0.6958 
Morph 
61.7268 
6.8127 
2.4276 
5.7014 
0.7191 
DWT 
58.9473 
7.5800 
1.8069 
1.7212 
0.6732 
SiDWT 
61.5045 
7.1970 
2.4766 
5.1124 
0.7235 
Trough the experiments we mainly focused on the LPT, 
Morph, DWT, SiDWT fusion algorithms. From the figure 3 we 
can find use the adaptive multi-objective optimization in the 
combing coefficients period as figure 2 demonstrates has much 
better quality than other method. From the origin image (Figure 
1), we find we can not differentiate water region and road 
region easily in SAR image which has low resolution and 
speckle noise. And the same time the SPOT image has high 
space resolution, but the problem is that the same ground object 
in the image has the different spectrum and brightness (e.g. 
water region).To get more correct information from the two 
origins (e.g. Land cover classification and enhance the 
resolution of the SAR image), the efficient fusion scheme is 
needed. Through the fusion methods tests we can definitely 
distinguish waster region and road from fused image use 
method (b) than method (a), and the crucial period is adaptive 
multi-objective optimization (AMO) in the coefficients 
combing to search the Pareto optimal weights as shown in Fig. 
3(b). Therefore, the approach to image fusion that uses AMO to 
search the adaptive fusion weights is optimal. This approach 
can save up the optic features of the images and consider the 
huge differences with SAR image, overcome the limitations of 
simple combing parameters, and obtain the optimal fusion 
performances. 
Meanwhile, table 2 shows the evaluation indices of the fused 
images from different multi-scale decomposition levels. From 
table 2, the MI quality metric matches evaluations more closely 
than do the other measures. Prefer the SiDWT the first and 
MORPH the second adopted fusion methods and the MI 
measure indicates highest values for these two fusion schemes. 
For poor fusion approaches like the LPT pyramids and so on, 
MI assigns them much lower values, matching evaluations. For 
the DWT fusion schemes, MI shows a low value which is quite 
lower than SiDWT close to the evaluation ratings from the 
observers. CE and UI measures match the trends but not 
remarkable in the cases. 
As one can expect, it seems that standard quality metrics like 
SD, to extract features from the fused image itself show no 
apparent correlation to compare fusion method and subjective 
evaluations. EN is used to measure global information content 
in the fused image. The EN metric produces comparable quality 
values for the DWT methods than others. This caused by the 
sensitivity of the EN metric to noise and other dramatic 
fluctuations in the image as perceptual. The entropy can not 
discriminate between useful information and noise. 
Furthermore, from the table 2 we can see the higher the 
decomposition level is, the MI metric decrease and other 
metrics have had no significant difference. These must caused 
by the quality of the SAR image which had some speckle noise 
and other special characteristics. As levels raised, more details 
get in consider and computer, this may cause the introduction of 
noise. 
The above comments are based upon the average values 
over some different region RS images. There may be some 
exceptions for some individuals. For most cases, the majority of 
information in the fused images is transferred from high 
resolution optical images, and the information got from the 
SAR is auxiliary but maybe crucial for the information 
extraction and other applications. 
5. CONCLUSIONS 
In the paper, through subjective and objective performance 
evaluations for the various fusion schemes applied in fusing 
SAR with optical images for the information extraction. The 
method using AMO to optimize the weight parameters of 
combining is feasible and relatively effective, which can get the 
Pareto optimal fusion result. Multi-objective optimization for 
the fusion parameters can avoid the limitations of too heavy 
dependence on the experience and so simple algorithm such as 
choose-max or weighted average methods. The experimental 
results show that man usually prefers the SiDWT or MORPH 
fusion approaches for the task. The tests of objective quality 
measures show that the Mutual Information (MI) better matches 
the purpose than do the others. 
One aspect that we would like to explore in the future is as 
follows. 
1) Using prior information. Our research employed little 
extra information about the source images other than the pixel 
values. If prior knowledge is available, we can attempt to use 
such information to improve the performance. Using prior 
information in the fusion is an important issue that should be 
studied. 
2) Research on the reduction of the metrics.There are so 
many quality metrics need studied in the world and which 
metrics combine is the most valuable and efficient. 
Sophisticated and efficient combination of quality measures 
should be a topic of future study. 
3) Multi-spectral or hyper-spectral images fusion with SAR 
for applications. The bands are Bi, B2, and B3 or more and the