Full text: Technical Commission III (B3)

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Fig.1 The curves of recognition rate vs. Dimension 
of different training samples of each approaches 
Tab.1 The max recognition rates on Washington DC 
  
  
Mall(%) 
Training Size NPE RPC PCA SRDA 
5x12 76.70 7911 7553 79.32 
10x12 85.87 8340 8369 8721 
15x12 890,57 3574 8552 8983 
20x12 9067 3770 83762 915] 
  
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
  
From Fig.1 and Tab.1, we can find that with increasing in 
the number of the training samples, the recognition rate 
increases. Also, we can notice that the SRDA shows the 
best performance around all the methods, that is to say, 
the SRDA, we proposed algorithm, always has the 
highest resolution. 
3.2 Experiments on AVIRIS Indian Pines 
In this section, we conduct our experiments in following 
steps: 
1) There are 16 labels in this dataset. In order to conduct 
the experiments reasonably, we reduce 6 labels whose 
sample numbers are all less than 320. Thus we need 
320*10 samples, and each sample contains 220 bands; 
2) As described above, for the chosen 320 samples, 1(=5, 
10, 15, 20) datum are randomly selected for training and 
the rest 300 datum are used for testing; 
3) The 5-nearest neighbor classifier is applied in PCA, 
NPE and SRDA subspace. For PCA and SRDA, the 
numbers of the subspace dimension are 40, while the 
NPE's are 14, 40, 40 and 40. The curves of recognition 
rate vs. dimension are shown in Fig.2. And the max 
recognition rates of each method are also reported in 
Tab.2. 
  
  
  
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Recognition rate 
  
  
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(b) 10 TP#ieréirg Samples 
    
    
  
    
     
     
     
   
   
  
    
   
  
    
  
  
     
      
   
    
   
  
Recognition rate 
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