## 3 Results
### Classification Results and Accuracy Assessment
#### 3.1.3 Accuracy Assessment  
In Section 3.1, based on the statistics of the winter wheat area under different machine-learning algorithms, we determined that the residual error of winter wheat area identified by the SVM algorithm is the smallest. In Tables 3-5, the error matrix of the three algorithms was used, and the OA, UA, PA, and Kappa indicators were calculated to compare the classification accuracy of the three algorithms. Figure 5: Winter wheat mapping in different regions using the three machine-learning algorithms. (**a–d**) The different regions of this study area. By observing the above tables, it can be seen that the OA, UA, and PA of the three classifiers are all above 80%, and the OA values of SVM and RF are above 90%. Therefore, these machine learning algorithms all achieve good classification accuracy. However, SVM and RF are more robust than CART. The value of OA is the ratio of the number of correctly classified samples to the total number, which can directly reflect the classification accuracy. The OA values of SVM, CART, and RF are 0.94, 0.93, and 0.87, respectively. This indicates that the number of correctly classified pixels of SVM is the largest, and OA is slightly higher than other two algorithms. Since the main purpose of this paper is to validate the accuracy of the three machine learning algorithms in identifying winter wheat, the UA and PA of winter wheat were calculated. For UA, the values of SVM, RF, and CART are 0.95, 0.94, and 0.93, while for PA, they are 0.95, 0.94, and 0.91, respectively. Thus, the classification accuracy of SVM is slightly higher than that of CART and RF. It can be found the values of the UA and PA of SVM have the largest values, which indicates that SVM can correctly classify winter wheat pixels with the largest proportion and has better classification performance compared to CART and RF. Kappa is a statistical parameter used to test the consistency of classifications, and it is able to take into account the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Classification**} & \multicolumn{8}{c}{**Reference**} \\ \cline{2-9}  & **WHE** & **VEG** & **URB** & **WAT** & **OTH** & **Total** & **UA** \\ \hline WHE & 250 & 16 & 0 & 0 & 0 & 266 & 0.94 \\ VEG & 13 & 253 & 5 & 1 & 1 & 273 & \\ URB & 2 & 10 & 127 & 0 & 4 & 143 & \\ WAT & 2 & 4 & 0 & 52 & 0 & 58 & \\ OTH & 0 & 1 & 0 & 0 & 90 & 91 & \\ Total & 267 & 284 & 132 & 53 & 95 & 831 & \\ PA & 0.94 & & & & & & \\ OA & 0.93 & Kappa & 0.90 & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: The error matrix of RF. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Classification**} & \multicolumn{8}{c}{**Reference**} \\ \cline{2-9}  & **WHE** & **VEG** & **URB** & **WAT** & **OTH** & **Total** & **UA** \\ \hline WHE & 250 & 16 & 0 & 0 & 0 & 266 & 0.94 \\ VEG & 13 & 253 & 5 & 1 & 1 & 273 & \\ URB & 2 & 10 & 127 & 0 & 4 & 143 & \\ WAT & 2 & 4 & 0 & 52 & 0 & 58 & \\ OTH & 0 & 1 & 0 & 0 & 90 & 91 & \\ Total & 267 & 284 & 132 & 53 & 95 & 831 & \\ PA & 0.94 & & & & & & \\ OA & 0.93 & Kappa & 0.90 & & & & \\ \hline \end{tabular}
\end{table} 
Table 5: The error matrix of CART. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Classification**} & \multicolumn{8}{c}{**Reference**} \\ \cline{2-9}  & **WHE** & **VEG** & **URB** & **WAT** & **OTH** & **Total** & **UA** \\ \hline WHE & 249 & 24 & 0 & 0 & 0 & 269 & 0.93 \\ VEG & 19 & 233 & 11 & 6 & 4 & 276 & \\ URB & 1 & 8 & 127 & 0 & 10 & 146 & \\ WAT & 0 & 5 & 0 & 55 & 1 & 61 & \\ OTH & 0 & 6 & 8 & 0 & 66 & 81 & \\ Total & 273 & 273 & 146 & 61 & 80 & 833 & \\ PA & 0.91 & & & & & & \\ OA & 0.87 & Kappa & 0.83 & & & & \\ \hline \end{tabular}
\end{table} 
Table 5: The error matrix of CART. differences caused by different proportions of classes. According to the above tables, the Kappa values of SVM, CART, and RF are 0.92, 0.90, and 0.83, respectively, indicating that the Kappa of SVM is also slightly higher than that of the other two algorithms.