# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 4 Conclusions  
This study attempted to classify different cultivars of winter wheat at Wuzhi, Henan province, by integrating DJI-M600 UAV and the UHD185 hyperspectral camera into a hyperspectral remote sensing system and comparing different data dimension reduction and classification methods. The experiment studied the dimension reduction method of the original hyperspectral imagery and then introduced machine learning and deep learning theory to study the fine classification method of different varieties of winter wheat. The experimental results were finally evaluated and discussed in qualitative and quantitative ways. The main conclusions are as follows:  
* In terms of different cultivars of winter wheat, this paper explored hyperspectral imagery classification results of the six different winter wheat types. The results showed that type 4 and type 6 winter wheat have the best classification results than other types with the average accuracy of each classification method are all above 84%, while type 2 and type 3 winter wheat have the lowest classification accuracy. The differences in classification accuracy can be explained by the separability of their spectral curves, which are related to chemical components and physical characteristics of different winter wheat cultivars. * In terms of dimensionality reduction methods, this paper used different dimensionality reduction methods (PCA, MNF, ICA) to reduce the redundancy data of hyperspectral imagery. The results show that there is a 30% improvement in classification accuracy \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Winter Wheat Cultivars**} & \multicolumn{3}{c}{**SVM (Linear)-MNF**} & \multicolumn{3}{c}{**MLE-MNF**} & \multicolumn{3}{c}{**ENVINet5-MNF**} \\ \cline{3-10}  & & **PA** & **UA** & **AA** & **PA** & **UA** & **AA** & **PA** & **UA** & **AA** \\ \hline
**1** & Aikang 58 & 74.93 & 83.89 & 79.41 & 74.4 & 83.5 & 78.73 & 88.38 & 79.8 & 84.09 \\
**2** & Bainong 207 & 71.13 & 72.46 & 71.80 & 66.41 & 74.11 & 70.26 & 73.6 & 77.74 & 75.67 \\
**3** & Jimai 22 & 85.27 & 71.83 & 78.55 & 70.52 & 65.55 & 68.04 & 92.25 & 68.73 & 80.49 \\
**4** & Jimai 8 & 98.65 & 81.15 & 89.9 & 96.72 & 71.55 & 84.14 & 99.41 & 82.69 & 91.05 \\
**5** & Nongda 5181 & 94.25 & 77.97 & 86.11 & 98.9 & 72.65 & 81.28 & 93.3 & 83.56 & 88.43 \\
**6** & Pumai 168 & 94.34 & 81.3 & 87.82 & 96.05 & 82.87 & 89.46 & 91.62 & 84.4 & 88.01 \\  & Other & 44.48 & 80.03 & 62.26 & 47.42 & 76.92 & 62.17 & 36.73 & 77.83 & 57.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracy table for each winter wheat cultivars (unit: %). after dimensionality reduction, and MNF has the best dimension reduction effect compared with PCA and ICA. * In terms of classification methods, the obtained data were classified by MLE, SVM (Linear), and ENVINet5 after dimensionality reduction. The combined MNF-ENVINet5 model showed the best classification results, and the OA and Kappa coefficients were 83%, and 0.81, respectively. This study improved the precision of fine classification of different types of winter wheat, which has weak intra-class features. This proposed hyperspectral data processing way may help to solve the problem of crop fine classification with typical weak intra-class features. Conceptualization, X.L. and W.D. ; methodology, H.Z. and Z.C. ; software, W.G. ; validation, W.D. and S.W. ; formal analysis, X.L. and W.D. ; investigation, X.L. ; resources, W.G. ; data curation, W.D. and S.W. ; writing--original draft preparation, X.L. ; writing--review and editing, X.L. and Z.C. ; visualization, X.L. ; supervision, W.D. and Z.C. ; project administration, H.Z. ; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.