# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 3 Results and Discussion
### Comparing Different Classification Methods
#### 3.3.3 Classification Results of ENVINet5  
To obtain the best classification results based on the ENVINet5 model, this study mainly analyzed the influence of different classification weight and loss weight parameters on the model. Since the effective value of the classification weight parameter is 0 to 6, its minimum value has been set to 0, and its maximum step size has been set to 2. Since the effective value of the loss weight parameter is 0 to 3, its step size has been set to 1, and the model Epoch has been set to 25. In the parameter setting of the deep learning model, even if the parameters are the same, the initial slice will influence each training, and decrease the training results [44]. The effect of the training model can be reflected by the loss value after multiple iterations, and the minimum loss value usually means the best classification weight and loss weight parameter combination. Thus, the minimum cross-entropy loss value was used as the final evaluation standard for the model training accuracy in this study. The results are shown in Table 4. It can be seen from Table 4 that when the classification weight is fixed, the cross-entropy loss increases with the increase of the loss weight. When the loss weight is fixed, the cross-entropy loss firstly increases and then decreases with the classification weight. After adjusting the parameters of loss weight and classification weight several times, the final cross-entropy value ranges from 0.09 to 0.3, indicating that the model training results need to be further improved. Among all the parameter adjusting results, when the classification weight was set to 2 and the loss weight was set to 0, the ENVINet5 training model showed the best classification effect with the minimum cross-entropy loss value was 0.09. For the optimal parameter training of the ENVINet5 model, the cross-entropy loss value during training was plotted as shown in Figure 12. From the figure, it can be seen that the cross-entropy loss value of the optimal parameter combination model does not show a decreasing trend in the iterative training process; there are some fluctuations, such as an abnormal increase in cross-entropy loss value during the third and fourth iterations. However, with the increase of iterations, the cross-entropy loss value reached a minimum value, which indicates that the model has reached optimal training status. After parameter analysis and adjustment, the optimal parameter combination of the ENVINet5 model was selected to classify the obtained hyperspectral images. In addition, previous results indicate that compared with ICA and PCA, the MNF dimension reduction method has the best dimension-reducing effect, thus, the number of bands participating in the training was selected as the number of bands after dimension reduction by MNF. The OA and Kappa coefficient are 83% and 0.81, respectively. The classification results are shown in Figure 13d. Different varieties of winter wheat can be classified, although misclassification and salt-and-pepper noise cannot be completely avoided. Among all varieties of winter wheat, type 2 and type 3 are more easily misclassified with each other, whereas type 4 and type 6 winter wheat have better classification results. The classification accuracy in classifying the field bank still needs to be improved. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Loss Weight**} & **Classification** & **The Cross-** & **Loss Weight** & **Classification** & **The Cross-** \\ \cline{2-6}  & **Weight** & **Entropy Loss** & **Weight** & **Weight** & **Entropy Loss** \\ \hline \multirow{4}{*}{0} & 0 & 0.12 & & 0 & 0.13 \\ \cline{2-6}  & 2 & 0.09 & & 2 & 0.27 \\ \cline{2-6}  & 4 & 0.13 & & 4 & 0.25 \\ \cline{2-6}  & 6 & 0.21 & & 6 & 0.23 \\ \hline \multirow{4}{*}{1} & 0 & 0.14 & & 0 & 0.17 \\ \cline{2-6}  & 2 & 0.23 & & 2 & 0.19 \\ \cline{1-1} \cline{2-6}  & 4 & 0.31 & & 4 & 0.24 \\ \cline{1-1} \cline{2-6}  & 6 & 0.24 & & 6 & 0.21 \\ \hline \hline \end{tabular}
\end{table}