Evaluation of conventional digital camera scenes for Thematic Information Extraction
(A) Area A
Kappa coefficient and overall accuracy results of the three measures of accuracy are
shown in Table 1. The overall accuracy is expressed as a percentage of the test-pixels
successfully assigned to the correct classes. The results obtained are presented in Tables
1, 2 and 3, where the overall classification accuracy, the confusion matrix and the
accuracy of each class using Maximum Likelihood, minimum distance-to-mean and
parallelepiped classification are given, respectively. From the present analysis, one can
see that the Maximum Likelihood classifier produced the best image classification
accuracy with the highest overall accuracy and Kappa coefficient. The overall
classification accuracies achieved by the proposed Maximum Likelihood classifier on the
digital image is 92.00 %. This followed by the Minimum Distance-to-Mean with the
overall classification accuracy of 85.50%, and Parallelepiped resulted in the overall
classification accuracy of 67.00%. A classified image using Maximum Likelihood
classifier is shown in Figure 4.
Table 1: The overall classification accuracy and Kappa coefficient
| Classification method |
Overall classification accuracy (%) |
Kappa coefficient |
| Maximum Likelihood |
92.00 |
0.884 |
| Minimum Distance-to-Mean |
88.50 |
0.832 |
| Parallelepiped |
67.000 |
0.561 |
Data
Supervised classification
was performed to the
digital images
Some samples training
sites were choose
Accuracy Assessment
Table 2: The confusion matrix results
| Classified Data |
Reference Data |
| Forest |
Water |
Water Turbid |
Land |
Total |
| Forest |
72 |
2 |
0 |
1 |
75 |
| Water |
3 |
65 |
3 |
1 |
72 |
| Turbid Water |
0 |
4 |
33 |
1 |
38 |
| Land |
0 |
0 |
0 |
14 |
15 |
| Total |
76 |
71 |
36 |
17 |
200 |
Table 3: The accuracy of each class using Maximum Likelihood classification.
| Class |
Maximum Likelihood |
| Producer Accuracy (%) |
User Accuracy (%) |
| Forest |
94.737 |
96.000 |
| Water |
91.549 |
90.278 |
| Turbid Water |
91.667 |
86.842 |
| Urban |
82.353 |
93.333 |