Table 1. Normalized results for the classification results 
obtained with the neural network algorithm 
for the classification using 5% of data for training. 
  
Reference categories 
  
  
Classification 
categories Com Soybeans Forest Pasture Bare Soil River 
Com 0.9465 0.0026 0.0317 0.0125 0.0011 0.0055 
Soybeans 0.0044 0.9275 0.0080 0.0454 0.0025 0.0123 
Forest 0.0065 0.0442 0.9389 0.0102 0.0001 0.0002 
Pasture 0.0012 0.0221 0.0006 0.8970 0.0789 0.0002 
Bare Soil 0.0297 0.0009 0.0010 0.0410 0.9271 0.0003 
River 0.0002 0.0001 0.0078 0.0001 0.0000 0.9918 
  
Table 2. Normalized results for the classification results 
obtained with the neural network algorithm 
for the classification using 10% of data for training. 
  
Reference categories 
  
  
Classification 
categories Com Soybeans Forest Pasture Bare Soil River 
Com 0.9528 0.0080 0.0122 0.0200 0.0006 0.0064 
Soybeans 0.0035 0.9121 0.0321 0.0412 0.0001 0.0111 
Forest 0.0185 0.0169 0.9408 0.0234 0.0001 0.0003 
Pasture 0.0007 0.0414 0.0029 0.9145 0.0404 0.0001 
Bare Soil 0.0019 0.0195 0.0005 0.0008 0.9770 0.0003 
River 0.0082 0.0001 0.0035 0.0001 0.0000 0.9880 
  
Table 3. Normalized results for the classification results 
obtained with the neural network algorithm 
for the classification using 15% of data for training. 
  
Reference categories 
  
  
Classification 
categories Com Soybeans Forest Pasture Bare Soil River 
Com 0.9607 0.0059 0.0072 0.0182 0.0012 0.0068 
Soybeans 0.0031 0.9619 0.0272 0.0075 0.0001 0.0002 
Forest 0.0146 0.0064 0.9561 0.0222 0.0003 0.0004 
Pasture 0.0005 0.0160 0.0010 0.9116 0.0586 0.0123 
Bare Soil 0.0009 0.0006 0.0052 0.0445 0.9486 0.0002 
River 0.0077 0.0001 0.0000 0.0001 0.0000 0.9920 
  
Table 4. Normalized results for the classification results 
obtained with the neural network algorithm 
for the classification using 20% of data for training. 
  
Reference categories 
  
  
Classification 
categories Com Soybeans Forest Pasture Bare Soil River 
Corn 0.9692 0.0043 0.0108 0.0082 0.0002 0.0073 
Soybeans 0.0026 0.9570 0.0066 0.0212 0.0028 0.0098 
Forest 0.0087 0.0016 0.9875 0.0013 0.0005 0.0004 
Pasture 0.0004 0.0226 0.0009 0.9505 0.0255 0.0001 
Bare Soil 0.0067 0.0062 0.0001 0.0122 0.9748 0.0001 
River 0.0001 0.0001 0.0026 0.0001 0.0000 0.9970 
  
532 
Table 5. Performance summary of 
the neural network classifiers corresponding 
to the trainings with 5%, 10%, 15%, and 20% TM data. 
  
  
  
Classification Classifiers 
categories 
NN-5% NN-10% NN-15% NN-20% 
Com 0.9465 0.9528 0.9607 0.9692 
Soybeans 0.9275 0.9121 0.9619 0.9570 
Forest 0.9389 0.9408 0.9561 0.9875 
Pasture 0.8970 0.9145 0.9116 0.9505 
Bare Soil 0.9271 0.9770 0.9486 0.9748 
River 0.9918 0.9880 0.9920 0.9970 
  
Table 6. SAS output from the multiple comparisons 
of the classifiers corresponding to the trainings 
with 5%, 10%, 15%, and 20% TM data. 
  
General Linear Models Procedure 
Tukey’s Studentized Range (HSD) Test for variable: Y 
NOTE: This test controls the type I experimentwise error rate, 
but generally has a higher type II error rate than REGWQ. 
Alpha- 0.05 df= 14 MSE= 0.00017 
Critical Value of Studentized Range=4.111 
Minimum Significant Difference= 0.0219 
Means with the same letter are not significantly different. 
  
  
  
Tukey Grouping Mean N METHOD 
A 0.9727 6 NN-20% 
A 
B A 0.9552 6 NN-15% 
B 
B 0.9475 6 NN-10% 
B 
B 0.9381 6 NN-5% 
60 = 
Training rate (seconds/cycle) 
  
  
NN-5% NN-10% 
NN-15% 
Figure 1. Training rates of 
the four neural network classifiers.