# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 3 Results and Discussion
### Comparing Different Classification Methods
#### 3.3.4 Comparing Different Classification Methods  
When comparing the classification results of machine learning with data without dimension reduction, machine learning (SVM and MLE) with data obtained after dimension reduction, and ENVINet5 model classification methods with data obtained after dimension reduction, the best classification results of each classification method are shown in Figure 13, and their R\({}^{2}\) and \(p\) value were acquired. From Figure 13, it can be seen that the classification of the ENVINet5 model, which is based on deep learning, has the best results, the following are MNF-SVM (linear) and MNF-MLE. Original-MLE has the poorest classification results, and it has serious misclassification and salt-and-pepper noise, thus it is difficult to realize the fine classification of winter wheat without basic dimension reduction data processing. In contrast, after dimension reduction, the misclassification and salt-and-pepper noise are reduced in the classification based on both machine learning and deep learning; especially in the classification of the ENVINet5 model, the misclassification and the salt-and-pepper noise has been significantly reduced. However, the classification of each type of winter wheat is excessively aggregated in the ENVINet5 model; as a result, the field bank between wheat is under-segmentation, and most field bank between wheat is misclassified into wheat, which is inferior to the first two machine learning-based classification methods. Statistical analysis results show that the R\({}^{2}\) of the MNF-ENVINet5 method is 0.96, the \(p\) value is 1.96518 \(\times\) 10\({}^{-4}\); the R\({}^{2}\) of the MNF-SVM (linear) method is 0.95, and the \(p\) value is 3.26785 \(\times\) 10\({}^{-4}\); the R\({}^{2}\) of the MNF-MLE method is 0.90, and the \(p\) value is 7.5626 \(\times\) 10\({}^{-4}\); and the R\({}^{2}\) of the Original-MLE method is 0.71, and the \(p\) value is 0.00649. In summary, it can be seen that the significance of the MNF-ENVINet5 method is higher.  
Figure 12: The cross-entropy loss value during ENVINet5 model training.  
Figure 13: The best classification results of different classification methods: (**a**) Original-MLE; (**b**) MNF-MLE; (**c**) MNF-SVM (linear); (**d**) MNF-ENVINet5.  
The producer accuracy (PA), user accuracy (UA), and average accuracy (AA) of the classified winter wheat were calculated based on the classification confusion matrix to conduct a better evaluation of the fine classification results of different types of winter wheat. Table 5 shows that type 4 and type 6 winter wheat have the best classification results, since the AA of each classification method is all above 84%, especially for the ENVINet5 classification of type 4, the AA of which is more than 91%. Type 5 also shows satisfactory classification results with the AA of each method fluctuating between 81% and 88%. Following this is type 1, the AA of which fluctuates between 78% and 84%. The AA of type 2 and type 3 winter wheat is the lowest for each classification method, which suggests there are many misclassifications.  
UAV-based hyperspectral remote sensing system was used in this study to obtain hyperspectral data during the winter wheat booting stage and create a corresponding sample database. It aims at achieving more accurate classification results of different winter wheat on a larger scale through training a large number of sample data. These sample data are helpful when future research attempts to identify different crops from more complex real-world scenarios, such as land reclamation mining area and farmland near urban buildings. Future research will focus more on the spectral data of different landforms and different crop growth stages, discover sensitive bands between these differences, and use related indices to expand these differences, thereby enlarging the data scope and improving the classification accuracy between different landforms during different growth stages.