Full text: Technical Commission III (B3)

   
   
  
  
  
   
  
    
       
  
   
  
  
  
  
   
   
  
    
     
  
  
   
  
    
   
  
  
   
   
    
  
  
   
   
    
   
  
   
  
   
   
  
  
   
   
  
      
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(b) Minimum Distance 
Classification 
     
T 
(c) Maximum (d) Mahalanobis 
le-based 
m m eut 
Classification Classification n 
Figure 3. Classification result in downtown area 
In the case of the supervised classification method, the 
classification about the vegetation, bare soil parts were carried 
out well, but the building parts with similar spectral information 
with the vinyl greenhouse showed the misclassification. But on 
the other hand, the similar vegetation, bare soil parts of the 
object attribute with the vinyl greenhouse showed 
misclassification except the building by the rule-based 
classification method. It means the misclassification features 
with different features, so the vinyl greenhouse was extracted 
by connection of the method, rule-based classification method 
in each supervised classification. Mask band was created using 
greenhouse which was extracted in supervised classification 
result. Vinyl greenhouse corresponding with result of rule- 
based classification was masked using the created mask band. 
This helped to obtain new classification image. 
As the results of the connection, the vegetation area showed 
overall, good extract results, but the parts which were missed in 
the rule-based classification were not included in the connection 
process also. In the downtown area, a considerable number of 
building parts which were misclassified due to the spectral 
information in the supervised classification method was reduced 
through the connection process, and the vegetation and bare soil 
parts which were misclassified with the vinyl greenhouse were 
removed in the rule-based classification results. But some parts 
which were misclassified from the supervised classification 
method and rule-based classification were not removed. Fig. 4 
showed the connected classification results in the downtown 
area. 
     
(b) Minimum Distance 
Classification 
(a) Visual Interpretation 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B1, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
     
(¢) Maximum Likelihood (d) Mahalanobis Distance 
Classification Classification 
Figure 4. Connection classification results 
in the downtown area 
(2) Analysis on the land cover detection accuracy 
For the land cover detection accuracy analysis, the 
Maharanobis distance classification which the vinyl greenhouse 
was classified most among the supervised classification 
methods was selected, and the accuracy analysis was carried out 
by comparing with the visual interpretation results. The 
accuracy analysis was carried out by calculating the distribution 
area of the vinyl greenhouse by using the area of each pixel. Fig. 
5 showed the area rate graph by each classification method 
based on the visual interpretation area. 
  
200 
  
  
  
  
  
  
  
  
= me em, 
  
  
  
  
  
   
  
  
50 TII 
60 
40 - 
23 
9 
Rule-based Connected- zd 
Disistance M d sec Classification N Missa 
ium 3 
Classification | Cassitication Method Interpretation 
Figure 5. Vinyl greenhouse distribution area 
by classification method 
If the visual interpretation area is 100%, Maharanobis distance 
classification is 187.1%, rule-based classification is 184.6%, 
and it can be seen that the vinyl greenhouse was over-classified 
than the existing distribution. In the case of Maharanobis 
distance classification, it is judged that the object with the 
similar spectral information was misclassified with the vinyl 
greenhouse, and in the case of the rule-based classification 
method, the attribution of the object such as the vegetation 
figure, form, size etc. is similar with the rule dividing the vinyl 
greenhouse and it is misclassified. In addition, it can be 
assumed that the quantity of misclassification is significantly 
reduced with 77% of visual interpretation areas for the 
connection of two methods, and it is judged that each part 
which was misclassified in the connection process was carried 
out with complementary.
	        
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