# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 3 Results and Discussion
### Comparing Different Classification Methods
#### 3.3.2 Classification Results of SVM  
The classification performance of SVM in hyperspectral imagery classification is easily affected by the type of kernel function and over-dimensional data. To avoid the effects of other parameters on classification results, the linear kernel with consistent parameters was used in SVM classification. The SVM (linear) classification was carried out under three dimension reduction conditions of PCA, ICA, and MNF. Table 3 shows OAs and Kappa coefficients. It shows that the classification accuracy without dimensionality reduction is less than 50%, whereas the classification accuracies after PCA/MNF/ICA dimension reduction are all above 74%. Compared with the classification accuracy without dimension reduction, the accuracy improves by about 30% after dimensionality reduction, which suggests that the classification of the full band imagery after dimension reduction can not only reduce the amount of data and improve the operation efficiency, but also improve the classification accuracy. Additionally, MNF-SVM (Linear) combination has the best classification accuracy: its OA and Kappa are 78.11% and 0.7449; PCA-SVM (Linear) combination has the second classification accuracy: its OA and Kappa are 74.15% and 0.6989; ICA-SVM(Linear) combination has the poor classification accuracy: its OA and Kappa are 74.14% and 0.6887.  
The classification results of SVM (linear) can, respectively, be seen in Figure 11. The SVM (linear) classification results are consistent with the previous classification results of the MLE classification method. When comparing PCA-SVM (linear) and MNF-SVM (linear) in Figure 11a,b), the former show more serious misclassification and salt-and-pepper noise; among all varieties of winter wheat, type 2, type 3, and type 5 are more easily misclassified by each other, especially in the classification between type 2 and type 3. PCA-SVM (linear) shows poor classification accuracy in classifying the field bank between wheat; some longitudinal earth among type 3, type 4, and type 5 are misclassified as winter wheat. In contrast, ICA-SVM (linear) shows better classification results of the field bank between wheat, although its overall classification accuracy is insufficient when compared with other methods. When comparing the classification results of different cultivars of winter wheat, it is clear that, based on the SVM (linear) classification method, type 2 and type 3 winter wheat show more misclassification and noise, whereas type 4 and type 6 winter wheat had better classification results.  
Figure 11: SVM (linear) results of different dimensionality reduction methods: (**a**) PCA-SVM (linear); (**b**) MNF-SVM (linear); (**c**) ICA-SVM (linear).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensionality Reduction** & **Classifier** & **Overall Accuracy** & **Kappa Coefficient** \\ \hline Original & SVM Linear & 46.88 & 0.3800 \\ MNF & SVM Linear & 78.11 & 0.7449 \\ ICA & SVM Linear & 74.14 & 0.6887 \\ PCA & SVM Linear & 74.15 & 0.6989 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification results based on SVM.