site, and the rule-based classification was carried out by using 
the spatial information and the texture information. In addition, 
the new classified image was created through the connection by 
extracting the house plantation in each result of the supervised 
classification and the result of the rule-based classification. The 
result of rule-based classification method was masked by the 
mask band created using the result of supervised classification 
method. 
Each classified image deciphered the classification degree by 
comparing the visual interpretation results based on the specific 
areas such as the vegetation area, downtown area etc. In 
addition, the accuracy was analyzed by comparing between the 
house plantation area and visual interpretation area of the 
classification results. 
   
  
Selection of study site 
    
  
  
      
   
   
  
  
= m 
sje acquis 
1 processing 
   
  
| | 
Supervised Rule-based 
classification classification 
  
  
  
   
Comparison by 
classification method |. 
  
    
    
    
  
“Efficient classification 
Efficient icem 
Figure 2. Flow Chart 
3. ANALYSIS OF IMAGE CLASSIFICATION 
AND RESULT 
For the vinyl greenhouse detection, two copies of Formosat-2 
image photographed in March, 20th, April, 8th and 15th 2008 
for including all Jeju areas were obtained, and it was treated as 
mosaic. The mosaic image was divided for including Seogwipo 
area as the study area, and the vinyl greenhouse was extracted 
through the image classification. 
3.1 Image Classification 
(1) Supervised classification 
If the user knows the information about the subjects, then the 
supervised classification method to acquire information about 
the unknown region based on the information must be effective. 
For the image classification by the supervised classification, the 
classification item was set by the sea, vegetation, buildings and 
bare soil, vinyl greenhouse, and the image classification was 
carried out by using the parallel-piped classification, minimum 
distance classification, maximum likelihood classification, 
Maharanobis distance classification. 
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 
  
  
  
  
  
  
  
  
  
  
  
  
Characteristic Symbol Class 
The Sea Sea | blue 
: Forest sea green 
Vegetation 
Grass green 
Buildings Building E | cyan 
Bare Soil Soil yellow 
Vinyl 1 red 
Vinyl greenhouse Vinyl 2 | magenta 
Vinyl 3 I maroon 
  
  
  
  
  
Table 1. Class of Image Classification 
(2) Rule-based classification 
The vinyl greenhouse was extracted by using the object-based, 
rule-based classification. The rule-based classification added 
the rule to create the rule about attribution for dividing the 
subjects such as the spatial information and spectral information 
etc. and to clearly distinguish the subjects (Biao et al., 2007). 
This study defined the rule to extract the vinyl greenhouse as 
follows. 
» NDVI value is lower than the value of vegetation. 
» Form is akin to a rectangle. 
» The form is no longer than the road. 
» [thas the constant area range. 
Based on the rule above, the range of the attribute value which 
can classify the attribution of the vinyl greenhouse most was 
designated. The designation about the range of the attribute 
value is based on the user's decision, so the best value should be 
selected through the various attempts. 
(3) Visual interpretation 
In order to be used as the comparison materials for the 
accuracy analysis of the image classification results, the vinyl 
greenhouse was extracted through the visual interpretation. 
3.2 RESULT ANALYSIS 
(1) Detection results by the classification method 
The results by each classification method were divided with 
the vegetation area, downtown area, so the misclassification 
degree and the features were examined. 
The image classification results of the parallel-piped 
classification of the supervised classification method were not 
good so it was excluded from the subjects of examination. 
In the vegetation area with only the vegetation and vinyl 
greenhouse, the classification of the vinyl greenhouse was 
carried out in the classification method used in the study. But in 
the rule-based classification, the minority of the objects of the 
vinyl greenhouse was not extracted. In the downtown area, the 
misclassification was found from all classification method to 
take part in. The building with the similar vinyl greenhouse and 
spectral information was misclassified with the vinyl 
greenhouse in the supervised classification method, and 
Maharanobis distance classification showed the best results. In 
the case of the rule-based classification, the vegetation, bare 
soil, building with the similar form of the object, vegetation 
index and the size with the vinyl greenhouse was misclassified 
as the vinyl greenhouse. Fig. 3 showed the classification results 
in the downtown area. 
    
  
  
  
  
  
  
     
  
    
  
  
  
  
  
  
   
   
  
  
  
   
  
   
  
  
  
  
    
   
   
   
  
  
    
   
   
   
     
   
   
  
   
  
   
   
   
    
    
   
    
     
  
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