## 3 Results
### Classification Results and Accuracy Assessment
#### 3.1.3 Accuracy Assessment
The above analysis concludes that the values of the OA, UA, PA, and Kappa of SVM and RF are higher than that of CART. In brief, based on the existing sample data, SVM and RF have stronger generalization ability, better robustness, and the best performance in this classification. The Performance and Mechanism of Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Tree (CART)  
In order to further explore the classification performance of SVM, RF, and CART using the existing sample data, this study created a training set (70%) and a validation set (30%). Fifty different random seeds were generated, which were combined into 50 groups of different training sets and validation sets. Furthermore, changes in the various accuracy indicators under different sample data for each algorithm were analyzed (Figure 6). Figure 6: _Cont._  
In Figure 6, although the values of the assessment indicators calculated by the three algorithms are varied, all values of the three algorithms are large (all values are above 0.8). It can be concluded that the three machine learning algorithms have better performance in winter wheat mapping. However, compared to CART, the four assessment indicators of SVM and RF are higher. Specifically, the average OA values of SVM, RF, and CART are 0.93, 0.92, and 0.88, respectively. Meanwhile, the average UA values are 0.95, 0.95, and 0.93, and the average PA values are 0.94, 0.94, and 0.93, respectively. Finally, the average Kappa values are 0.90, 0.89, and 0.84, respectively. Obviously, through inputting the 50 different training and validation set combinations into the three algorithms, it is shown that when training the different data sets, both the SVM and RF algorithms achieve higher classification accuracy, and the interquartile range values of the four classification indicators are more concentrated. From a statistical point of view [60], the accuracy values obtained by SVM and RF have stronger generalization ability. In comparison, the interquartile range of CART is larger and the box body is longer, indicating that with the same sample data, the variation of CART is relatively large and the algorithm is not robust. During classification, the optimal hyperplane of SVM serves as a condition to classify different data labels. The way in which the optimal hyperplane and kernel function are determined is an important factor for the classification results. When processing binary classification data, linear classification can be utilized to obtain better classification accuracy. When the feature dimension is multi-dimensional, non-linear classification is required. The cost parameter C should be considered in both linear and non-linear classifications. The value of the parameter C directly affects the maximization of the decision boundary of SVM, thereby affecting the number of misclassified pixels [19; 48]. In addition, the choice of kernel function also affects the classification accuracy of SVM. SVM provides linear kernel function, polynomial kernel function, radial kernel function, etc. For this classification, we chose the classification radial basis function (RBF) \(k_{RBF}(x,x^{\prime})=\exp\bigl{(}-gamma\|x-x^{\prime}\|^{2}_{2}\bigr{)}\). This kernel function has been used in many classification research works and has achieved good classification results [19]. The choice of kernel function affects the classification performance of SVM, and the value of the kernel function parameter gamma has an impact on the classification accuracy. If gamma is small, the decision boundary is close to linear, which affects the non-linear classification data; however, when gamma is large, it causes overfitting [48]. In order to obtain the hyperparameters (C and gamma), this research utilized 5-fold cross-validation and the grid search method to tune the parameters, based on the Python scikit-learn library. Finally, we obtained the hyperparameters C:10 and gamma:10. Unlike the SVM algorithm mechanism, the CART and RF algorithm are both tree-based algorithms.