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The cutting from vector data above used to calculate accuracy level from classification of both methods back propagation neural network and maximum
likelihood. On the calculation method of confusion matrix, there are 3 types of accuracy that can be quantified, i.e. overall accuracy, producer’s accuracy and
user’s accuracy. Producer’s accuracy is calculated by dividing the number of correctly classified samples by the column total or the class in the confusion
matrix. User’s accuracy is calculated by dividing the number of correctly classified samples by the row total for the category in the confusion matrix.
Overall calculation can be seen in Table 4.6 and 4.7 In Table 4.5 below, it can be observed that producer’s accuracy and user’s
accuracy of both methods. Table 4.5. The Producer’s Accuracy and User’s Accuracy for Back Propagation
and Maximum Likelihood Classification Methods
Back Propagation Maximum Likelihood
Class Prod. Acc
User. Acc Prod. Acc
User. Acc
Tea Garden 61.6
87.9 76.9
70 Settlement 95.9
97.4 94.5 95
Paddy Field 62.9
93.4 36.4
85 Grass
98.8 91.4 94.2 55.9
Forest 96.4 73.3
71.4 76.8
Farm 78.3 41.5
81.2 20.9
Bush 70.8 36.2
60 22.7
Water Body 47.2 11.3
11.1 7.41
Table 4.6. The Confusion Matrix for Back Propagation Neural Network with 4000 Samples and 1000 Iteration
REFERENCE DATA Class
Tea Garden Settlement
Paddy Field Grass
Forest Farm
Bush Water
Row Total pixel
Tea Garden 648
44 36
1 6
2 737
Settlement
22
1437
17 1476
Paddy Field 18
6 545
7 10
4 590
Grass 3
4 85
1 93
Forest 343
5 964
3 1315
Farm 5
38 26
54 5
2 130
Bush 11
4 123
2 85
10 235
Water
4 10
102 1
5 11
17
150
Colum Total pixel
1051 1498
866 86
1000 69
120 36
4726
Producers Acc.
61.66 95.93
62.93 98.84
96.40 78.26
70.83 47.22
Users Acc.
87.92
97.36
92.37 91.40
73.31 41.54
36.17
11.33
C L
A
S S
I F
I E
D
D A
T A
Overall Acc. 81.15
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REFERENCE DATA
Class
Tea Garden Settlement
Paddy Field Grass
Forest Farm
Bush Water
Row Total pixel Tea Garden
809
49 2
285 1
7 1
1154
Settlement 8
1415 60
2 2
1 1488
Paddy Field
18 2
315
1 3
22 9
370
Grass 6
5 37
81 4
9 3
145
Forest 196
1 14
1 714
3 1
930
Farm 4
68 130
1 56
1 8
268
Bush 8
225 1
2 72
9 317
Water 2
7 36
1 4
4 54
Colum Total pixel 1051
1498 866
86 1000
69 120
36 4726
Producers Acc.
76.97
94.46
36.37 94.19
71.40 81.16
60.00
11.11 Users Acc.
70.10 95.09
85.14 55.86
76.77 20.90
22.71 7.41
C L
A
S S
I F
I E
D
D A
T A
Overall Acc. 73.34
Based on Table 4.5, it is shown that for classification using neural network method, the highest producer’s accuracy percentage was on grass class 98 and
the lowest percentage was on water body class 42. Meanwhile, for the classification using maximum likelihood method, the highest producer’s accuracy
was on settlement class 94.5 and the lowest accuracy was on water body class 11.1. These both methods back propagation neural network and maximum
likelihood can classify grass and settlement well; it is proved that the accuracy percentage is high. Grass class and settlement was categorized as good due to the
percentage resulted from these classes is 98.8 and 94.5 subsequently, it is higher then level of overall accuracy from both methods, which are 81.2 for
neural network and 73.3 fro maximum likelihood. Meanwhile, for the both methods, the lowest percentage was on water body
class, which is 42 for back propagation neural network and 11 for maximum likelihood method. This condition illustrates us that both methods have difficulties
in identifying that particular class. Some reason that can cause it, i.e. because of spectral value for water class is almost the same with spectral class of paddy field,
moreover if the paddy field is covered by water when the scan process of the sensor. The mistake can also due to thin cloud that cover the water body class,
which are not corrected by using radiometric correction. Table 4.5 is also describing user’s accuracy for both methods back
propagation neural network and maximum likelihood. For back propagation neural network method, the highest user’s accuracy was on settlement class
97.4, while for maximum likelihood method was also on settlement class 95. This means, for settlement class determination of reference data is almost
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the same with class determination of classification result. Meanwhile, the lowest user’s accuracy was on water body 7.41 and 11.3 for maximum likelihood
and back propagation neural network method. From the percentages above, the determination of water body class sample of classification result is not suitable
with the determination of reference data. This condition occurs due to the limitation in identifying water in reference data Ikonos 2001. The obtained water
sample just for river area due to it is assured as water spectral value .
An error matrix is a very effective way to represent map accuracy in that the individual accuracies of each category are plainly described along with both
the error of inclusion commission errors and errors of exclusion omission errors present in the classification Congalton, 1987. A commission error is
simply defined as including an area into a category when it does not belong to that category. An omission error is excluding that area from the category in which it
truly does belong. Every error is an omission from the correct category and commission to a wrong category.
In back propagation neural network method, all classes have omission error, but only on paddy field class from reference there are all classes that was
classified tea garden, settlement, grass, forest, farm, bush and water. Based on the reference, an area is expressed filled by a whole paddy field, but the fact from
the class cutting of classification result of neural network, there area 44 pixels was expressed as tea garden, 17 pixels as settlement, 4 pixels as grass, 5 pixel as
forest, 26 pixel as farm, 123 pixel as bush and 102 pixels as water body. This condition illustrated that back propagation has encountered difficulties in
differentiating paddy field and other classes. Whereas, in maximum likelihood
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method, omission error was also found in almost all category of classes, but paddy field and tea garden class have more level of variation. Based on producer’s
accuracy, paddy field has accuracy that can be classified as low under overall accuracy compared to tea garden. So, with the variation of class category that is
not included in paddy class, then it will give contribution to the level of its accuracy.
For both method back propagation neural network and maximum likelihood, all category classes have commission error, but none of them taken
from classification result contains all categories of other classes. Water class has commission error as follows: 2 pixel that supposed to be tea garden, 7 pixel in the
settlement, 36 pixel in the paddy field, 1 pixel as farm, 4 pixels as bush and 4 pixels as water body. This condition will give effect to the classification result,
which is supposed to be in water body class 4 pixels but it is identified as paddy field 36 pixels. This error occurred for both methods maximum likelihood and
back propagation neural network. Error omission diagram can be seen in this Figure 4.17 below.
200 400
600 800
1000 1200
1400
Number of Pixels
Tea Garden
Settlement Paddy
Field Grass
Forest Farm
Bush Water
Reference
Ommision Errors
Tea Garden Settlement
Paddy Field Grass
Forest Farm
Bush Water
Figure 4.17. Ommission Error for Back Propagation Neural Network Method
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72
200 400
600 800
1000 1200
1400
Number of Pixels
Tea Garden Settlement Paddy Field Grass
Forest Farm
Bush Water
Reference
Ommision Errors
Tea Garden Settlement
Paddy Field Grass
Forest Farm
Bush Water
Figure 4.18. Ommission Error for Maximum Likelihood Method
4.4 Comparison Back Propagation Neural Network and Maximum
