# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Data Classification Methods  
Maximum likelihood estimate (MLE), support vector machine (SVM), and the improved ENVINet5 classification model based on U-Net neural network structure were, respectively, used to conduct the fine classification of the obtained hyperspectral imagery. Overall accuracy and Kappa coefficient were used to evaluate the classification results. MLE is a classification algorithm based on normal Gaussian distribution. It is one of the most frequently used supervised classification methods. Its discriminant function is in Equation (1):  
\[L_{i}(x)=\mathrm{P}(\omega_{i}|x)=\frac{\mathrm{P}(x|\omega_{i})\cdot\mathrm{P} (\omega_{i})}{\mathrm{P}(x)},(i=1,2,\cdots,c) \tag{1}\]  
In Equation (1), \(L_{i}(x)\) is the probabilistic discriminant function of class \(i\). \(\mathrm{P}(x|\omega_{i})\) is the conditional probability of \(x\) occurring under the condition of category \(\omega_{i}\), which is expressed as the probability that vector \(x\) belongs to category \(\omega_{i}\). \(\mathrm{P}(\omega_{i})\) is the prior probability of category \(\omega_{i}\), \(\mathrm{P}(x)\) is the probability density function of \(x\). SVM is a machine learning algorithm based on statistical learning theory. It has the advantages of higher classification accuracy and better generalization ability compared with other traditional classifiers. In addition, SVM can also effectively reduce the dimension disaster, especially for classifying data with high dimensions and small samples [36]. It can be simplified as an optimal decision function in Equation (2):  
\[\mathrm{f}(\mathrm{x})=\mathrm{sign}\left(\sum_{i=1}^{n}a_{i}y_{i}(x_{i}\cdot x )+b\right) \tag{2}\]  
In Equation (2), \(x_{i}\) is the support vector, \(a_{i}\) is the Lagrange multiplier, \(y\) is the label for categories, \(y_{i}\) can be 1 or \(-1\), \(b\) is the classification threshold, and \(n\) is the number of support vectors. In application, it is important to choose the appropriate kernel functions of SVM to get better classification results. This study selected linear kernel, polynomial kernel, RBF kernel, and sigmoid kernel of SVM to process hyperspectral data classification. SVM with linear kernel function had the highest overall accuracy and Kappa coefficient when the C value and gamma value were set to 5 and 0.5, respectively. Thus, it was used for the following study, and the classification results of the SVM classifiers with  
Figure 7: Spectral imagery band eigenvalue transformation results of each dimension reduction method: (**a**) Transformation results based on PCA; (**b**) Transformation results based on MNF; (**c**) Transformation results based on ICA. different kernel functions are shown in Table A1. In addition, both MLE and SVM were implemented through ENVI and MATLAB in this study. Firstly, the hyperspectral image was converted into a matrix format, where each rectangular block represented a sample and each sample contained multiple spectral bands. Meanwhile, each sample was labeled with its corresponding cultivars. Secondly, the spectral feature of each sample was extracted, and then the cross-validation method was used to divide these samples into training and testing sets, and the classifiers were trained by using the training set. Finally, the performance of the trained SVM and MLE classifiers was evaluated by using the testing set, and the overall accuracy and Kappa coefficient were used to assess the classification results. ENVINet5 classification model was developed by the Environmental Systems Research Institute (ESRI) based on the TensorFlow deep learning framework. Since 2019, ENVI deep learning module has been released in three versions, which were Deep Learning V1.0, Deep Learning V1.1 Tech Preview, and Deep Learning V1.1.2. Compared with the first two versions, Deep Learning V1.1.2 has enhanced parameter settings for stronger learning abilities, for example, it adds Augmentation as the new training tool, which can not only expand the training data but also improve the training and extraction accuracy.