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2.5 Evaluation of Classifications 
Multiple comparisons were made to evaluate the four 
classifiers, including NN-5%, NN-10%, NN-15%, and 
NN-20%. By extracting the correct percentages of each 
classification category from Tables 1 through 4, we pro- 
duced a performance summary table of classifiers (Table 
5). The Tukey multiple comparison method was used 
for the evaluation of these four classifiers. Any two 
population means of classifiers will be judged to be dif- 
ferent from each other if the difference of the 
corresponding sample means is greater than the Tukey 
distance (Mendenhall and Sincich, 1989). The Tukey 
multiple comparison method is supported by SAS 
software (SAS Institute, 1988b). 
3. RESULTS 
The results of the Tukey multiple comparisons (Table 6) 
provided the overall classification accuracies for the 
classifiers of NN-5%, NN-10%, NN-15%, and NN-20%. 
At a risk level of 5%, the results showed that (a) 
classifiers NN-5%, NN-10%, and NN-15% did not differ 
from one another, (b) classifiers NN-15% and NN-20% 
did not differ from each other, but (c) classifiers NN-596 
and NN-10% differed from classifier NN-20%. The 
training rates of the neural network classifiers are illus- 
trated in Figure 1. 
4. DISCUSSION 
As shown in table 8, we could train the neural network 
with either 5%, 10%, or 15% TM data because the sta- 
tistical evaluation showed no significant differences 
among the three corresponding classifiers at a 5% risk 
level. The evaluation was done based on individual 
category accuracies highlighted in Tables 1 through 4. 
However, interpretations of classified images (Figure 2) 
show that the two classification results of NN-10% and 
NN-15% were more uniform than the result of NN-5%. 
We could not interpret that the classification result of 
NN-20% differed from the results of NN-10% and NN- 
15%. 
As we increased the percentage of the TM data for train- 
ing, the corresponding training rate also increased (Fig- 
ure 1). When we increased 5% to 10% for training, the 
training rate was decreased by 2 seconds/cycle. When 
we increased 10% to 15% or 20%, the training rate was 
reduced by 11 or 16 seconds/cycle, respectively. In this 
project, the training periods ranged from 100 to 200 
cycles. 
CONCLUSIONS 
Considering the evaluation results and the training rates, 
we recommand using around 10% TM data to train a 
neural network, and the performance of a neural net- 
work classifier is satisfactory for this level of training. 
531 
5. ACKNOWLEDGEMENTS 
This research was supported by NASA Research Grant 
NAGW-1472 and the Laboratory for Applications of 
Remote Sensing at Purdue University. 
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