4.4 Comparison Back Propagation Neural Network and Maximum
Likelihood Classification Method
The calculation of kappa statistic will also be performed in this research, besides accuracy calculation and matrix error
. The Kappa analysis is a discrete
multivariate technique used in accuracy assessment for statistically determining if one error matrix is significantly different than another Bishop et al, 1975. The
kappa statistic describes the difference between the observed classification accuracy and the theoretical chance agreement, which would result from a random
classification. This statistic serves as an indicator of the extent of to which the percentage correct values of an error matrix are due to “true” agreement versus
“chance” agreement Lillesand, 1994 The kappa statistic ranges between 0-1. If kappa statistic = 1, it is the
ideal case. The kappa statistic for maximum likelihood = 0.661 and back propagation = 0.759. The back propagation neural network is almost ideal case.
Table 4.8. The Comparison Two Classification Method
No Class. Method
Overall Accuracy Kappa Stat.
1 Back Propagation NN
81.15 0.759
2 Maximum Likelihood
73.34 0.661
As shown in table 4.10, both classifications back propagation neural network and maximum likelihood have a significant difference in level of
accuracy. From overall accuracy and kappa statistic, it is seen that the classification using back propagation neural network method 81.15, 0.759 of
Ciliwung watershed is better than using maximum likelihood method 73.34, 0.661. As well as with overall accuracy maximum likelihood is better than back
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propagation. Its means there are more pixels separate to another class category. Due to in this research there are 2 kinds of methods which is compared each other;
then kappa statistic can explain the different between both of it. From the value of kappa statistic, it can be seen that back propagation neural network method is
significantly different with maximum likelihood method. Kappa value of classification method using back propagation neural network is close to 1 0.759
compared to classification method using maximum likelihood method 0.661. Then, it can be concluded that back propagation neural network is nearly close to
ideal case. Ideal case means random sample acquiring from classification result will have similar result or near to the value of referenced sample.
From the accuracy measurement in above, it can be concluded that back propagation neural network method in Ciliwung watershed, which has varied
topography types, generates more accurate result compared to maximum likelihood method.
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Figure 4.19. Landuse Mapping in Ciliwung Watershed
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V. CONCLUSION AND RECOMMENDATIONS
5.1 Conclusions
a. The two classification methods maximum likelihood and back propagation neural network have been compared for landuse
classification. The landuse were classified into 8 classes that are tea garden, settlement, bush, farm, paddy field, grass, forest, and water
body. Even though the classification parameters used in this research was same, which are spectral value of six band band 1,
band 2, band 3, band 4, band 5 and band 7 contained in Landsat image, but it give different classification results.
b. Back propagation neural network method had a better accuracy compared to maximum likelihood method, which are 81.5 and
73.34. Both of method is too difficult to distinguish a water class and paddy field class category. Kappa statistic has also shown that
classification result using back propagation neural network 0.759 is visibly different with maximum likelihood method 0.661. The
classification result using back propagation neural network is nearly close to the real field condition.
5.2. Recommendations
In order to improve the scope and the accuracy of the classification, it is recommended to use several types of image with
different time. In addition, it is also recommended to set many more landuse classes by using larger training areas.
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