## 4 Methodology
### Accuracy Assessment and Classification Results Comparison  
Standard accuracy assessment parameters for class metrics, namely precision, recall, F1-score, and intersection over union (IoU) (Equations (7)-(10)) were computed for the predicted images using metrics of true positive (TP), false positive (FP), and false negative (FN) [45]. Overall accuracy (OA) [46] and kappa [47] were evaluation metrics for the classification (Equations (11) and (12) [48]).  
**TP** is when the prediction and example are positive. **FP** is when the prediction is positive but the example is negative. **FN** is when the prediction is negative but the example is positive. **TN** is the prediction and example are negative. **IoU** is calculated for all classes separately, and it is equal to the ratio of correctly classified pixels to the total number of ground truth and the predicted pixels. **OA** is an estimation of the percentage of correctly classified pixels. **Av. FI** is the average of F1-score [49] of all classes that were calculated in all images.  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[F1\;score=2\frac{Precision\;Recall}{Precision+Recall} \tag{9}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{10}\]  
\[OA=\sum_{i=1}^{K}\frac{Nii}{N} \tag{11}\]  
\[Kappa=\frac{n\sum_{i=1}^{d}n_{ii}-\sum_{i=1}^{d}n_{ki}n_{ci}}{n^{2}-\sum_{i=1}^ {d}n_{Ri}n_{ci}}\times 100 \tag{12}\]  
One of the methods used to test whether the values or findings obtained from research are statistically significant is the McNemar test [50]. The statistical significance of the difference between the two proportions can be measured using [PERSON]'s test for dependent variables. The McNemar test depends on the chi-square distribution and uses a 2 \(\times\) 2 dimensional error matrix in calculations [51]. In thematic mapping studies with the image classification method, accuracy assessment is conducted for different classifiers first. Then, the statistical significance of the difference between the accuracy calculations can be determined using the kappa coefficient and McNemar test [51]. The calculation ofMcNamar test value is shown in Equation (13), where \(f_{21}\) indicates the number of samples that were classified incorrectly (i, j = 1, 2) and a \(x^{2}\) value is obtained.  
\[x^{2}=\frac{\left(f_{12}-f_{21}\right)^{2}}{\left(f_{12}+f_{21}\right)} \tag{13}\]  
For this analysis, 2 \(\times\) 2 contingency tables (Table 3) were calculated by using the study's results of each model and reference data for different test sites. When \(H_{0}\) hypothesis is accepted, that means two different methods do not have a significant difference. If the value of calculated \(\chi^{2}\) is greater than the value of \(\chi^{2}\) in the table, the \(H_{0}\) hypothesis is rejected. If the value of \(\chi^{2}\) found by calculation is lower than the value of \(\chi^{2}\) in the McNemar test table, the \(H_{0}\) hypothesis is accepted. Table values are constant; e.g., for 95% accuracy confidence; the table value is 3841. If the null hypothesis \(H_{0}\) is true, paired models are the same, or neither of the two models performs better than the other one. Thus, when \(H_{0}\) is true, it means the two methods do not have a significant difference. On the other hand, if the \(H_{1}\) hypothesis is true, there is a significant difference between the two models.