204 
Fig3: image of HIS fusion method 
3. CLASSIFICATIONS AND ANALYSIS 
class 
road 
other 
Produce 
accuracy 
Total 
accuracy 
PCA 
method 
road 
163 
8 
95.3% 
85% 
other 
37 
82 
68.9% 
User 
accuracy 
81.5% 
91.1% 
HIS 
method 
road 
141 
18 
88.7% 
80% 
other 
42 
99 
70.2% 
User 
accuracy 
77% 
84.6% 
TM 
spectral 
image 
road 
130 
23 
85% 
77% 
other 
46 
101 
68.7% 
User 
accuracy 
73.9% 
81.5% 
Table 1. Error matrix and accuracy appraisement table of 
different fusion approaches 
Because of the special geology of the study area, we adopted 
computer automatic processing combined with visual 
interpretation. 
Comparing TM image to SAR image, it is clear for us to tell the 
difference between them. Though there is plenty of color 
information in TM image, it is difficult to differentiate road 
from other objects in such high vegetation. While in SAR 
image, with the smooth surface of the roads, the roads have 
lower coarseness than the other objects. So it is easy to 
recognize the roads. We respectively chose training samples for 
roads and other objects, and then applied the maximum 
likelihood classifier. The result images are shown in Fig4-6. 
Fig6: classification result of HIS method 
3.1 Visual Appraisement 
The three main roads in the area are recognized continuously 
and clearly from the principle component analysis fusion image, 
even the narrow paths in the reed mostly interpreted. 
Fragmentary tiny paths in the reed are extracted from the TM 
spectral image, while the aqueduct aside the road is recognized 
as road by error in the south of the area. The result of the HIS 
fusion method is put into the shade of the PCA method. Part of 
the paths in the reed is classified. 
3.2 Accuracy Analysis 
In order to compare the three classifications accurately, we 
select 300 samples randomly and get error matrix just as table 
1 .The overall accuracy of PCA method is 85%, being the best 
among the three classifications. HIS method’s accuracy is 80%, 
lower than PCA method, higher than spectral image which is 
77%. 
4. CONCLUSIONS 
Features from individual sensor images are not only preserved 
but also enhanced. With both spectral features and penetrability, 
tiny objects can be extracted from the fusion image of TM and 
SAR. We applied two fusion methods-PCA method and HIS 
method to obtain the fusion product. Then we use the 
maximum likelihood classifier to classify the images. We get 
results as follows. 
Instead of replacing PC-1, we replaced PC-5 by SAR image 
and obtained classification with the highest accuracy in the 
high vegetation region. Experimental results show that PCA 
method is suitable for the study area. 
The accuracy of HIS conversion method is higher than that of 
TM spectral image, but lower than PCA method. 
As a result, in the region with flourishing vegetation or being 
difficult to acquired remote sensing data, in order to get 
accurate data, it is feasible for us to apply fusion method of 
radar image and multi-spectral image to extract tiny objects.