15040€ — 15042 150°44'E 150*46'E i5Q"48'E  150°S0°E 
8.8.9¢ 
34°10'8 
8.01.05 
34175 
SZLoLE 
34148 
S ?Ll.v£ 
34"16'$ 
$91.r£ 
  
450°4WE  150°4ZE — 15044E — 150 49E — TSO'4EE — THRE 
  
sp Bl us RU © ws 
  
NP ; 
  
Figure 4. Results of classification using FS-GA with 
SVM classifier. 
The improvements of classification accuracy by using FS-GA 
technique for different classifiers and combined datasets is 
shown in the Figure 5. 
  
Improvements of classification accuracy 
by appying GA feature selection techniques 
  
  
SN 
in 
3 SVM 
M 
MANN 
8 SOM 
Improvements of acuracy (%) 
  
  
Datasets 
  
  
  
Figure 5. Improvements of accuracy by applying FA-GA 
techniques for SVM, ANN and SOM classifiers. 
The comparison of classification results between the best 
classifier in non-FS, FS-GA approach and the multiple classifier 
combination using Dempster-Shafer theory (FS-GA-DS) is 
given in the table 4 and figure 6. 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
  
Classifiation accuracy using non-FS, FS-GA and FS-GA-DS methods 
  
  
© 
2 
  
- 
© 
vds Non-FS 
be FS-GA 
exiesFS-GA-DS | 
  
Overall classification accuracy 
e x 
w 4 
  
e 
+ 
  
  
  
  
  
  
  
59 
  
  
Figure 6. Comparison of best classification results of non-FS, 
FS-GA and FS-GA-DS methods on different datasets. 
Although the FS-GA model has already produced significant 
increase in the classification accuracy of evaluated multisource 
remote sensing datasets, the integration of multiple classifiers 
combination with FS-GA method has even further remarkably 
improved the classification performance. The FS-GA-DS 
algorithm always gave better accuracy than any best single 
classifier in all cases. The range of classification improvements 
was from 1.24% for the 2"* dataset to 3.07% for the 4™ dataset 
as compared to the FS-GA model. Of course, increases in 
classification are even much more significant as compared to 
the traditional non-FS method. The highest classification 
accuracy obtained by the FS-GA-DS model was 88.29% with 
the largest combined datasets. The comparison of improvements 
in classification performance between FS-GA and non-FS; FS- 
GA-DS and FS-GA was given in the figure 7 below. 
One of the probably reason behind the successful of the FS-GA- 
DS model is its capability to integrate various optimal (or nearly 
optimal) solutions given by the GA method for specific 
classifier such as SVM, ANN or SOM to enhance the generality 
of the final solution. 
improvements of classifiation acuracy 
  
  
  
# FS-GA vs Non-FS 
B FS-GA vs FS-GA-DS 
Improvements (26) 
  
  
Datasets 
  
  
  
Figure 7. Improvements of overall classification accuracy 
achieved by using FS-GA and FS-GA-DS model. 
  
  
  
  
  
  
  
  
   
  
   
   
    
    
   
   
     
   
    
  
   
     
   
   
  
  
   
   
  
  
  
   
  
   
    
    
  
     
  
  
  
Overall classification accuracy (%) 
Datasets The Majority Voting (MV) algorithm is also very effective for 
Non-FS FS-GA FS-GA-DS combining classification results. However, it gave a slightly 
1 59.39 61.15 62.56 lower accuracy than the DS algorithm. Results of MV and DS 
algorithms were shown in the Table 5. 
2 79.97 81.06 82.30 
3 81.47 82.37 83.80 Algorithm Datasets 
1 1 2 3 4 
82.78 85.22 88.29 DS 62.56 82.30 83.80 88.29 
Table 4. Comparison of best classification results using non-FS, MV 61.98 82.30 83.52 88.22 
FS-GA and FS-GA-DS classifier combination method. 
  
  
  
  
  
    
  
  
Table 5. Classification accuracy by applying DS and MV 
algorithm for classifier combination.