# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Data Classification Methods
Deep Learning V1.1.2 was officially released in October 2020. It needs to be adapted to the latest ENVI5.6. Thus, ENVI5.6 and ENVI Deep learning1.1.2 modules were installed and activated from the ENVI official website to run the ENVINet5 classification model for this study. The basic architecture design of the ENVINet5 neural network model is based on the improved U-Net neural network structure. U-Net neural network is a classical implementation and semantic segmentation algorithm, which is based on fully convolutional neural networks (FCN). It mainly includes conv, max pool, up-conv, and ReLU activation functions as shown in Figure 8. Its operating mechanism can mainly be divided into two parts. The left half is the down-sampling part, which focuses on data feature extraction. The right half is the up-sampling part, in which extracted feature information is mapped to the category image. To realize pixel-level classification of images and keep image context information to higher resolution feature images, U-Net associates the feature image extracted from the lower sampling part with the upper sampling part to make up for the lost context information [37]. Thus, the most important improvement of U-Net is in the up-sampling operation part [38]. The advantages of the ENVINet5 classification model are that it can be combined with the powerful ENVI imagery processing platform, and it can be used to build training samples, do model initialization, train models, and run deep neural networks without any code operations. This is convenient for researchers and users, who hope to apply remote  
Figure 8: Illustration of complete architecture of network model. sensing and classify remote sensing imagery without deep learning foundations. The module used in this study mainly consists of four steps: creating training samples, creating models, training models, and image classification. The first three steps are mainly used for model training and parameter setting. For image slicing in deep learning, manual slicing is not required in the ENVINet5 model; instead, by inputting the images to be sliced or the representative parts of the images, and selecting appropriate parameters of slicing size, the ENVINet5 model will automatically perform slicing on the input images. In addition, the ENVINet5 model adopts an inverse transformation sampling method and adds a weight parameter when selecting a large number of slices. This not only reduces the number of iterations during training and improves training efficiency but also enables the trained model to better select slices containing target elements. The ENVINet5 model is sensitive to parameters during the sample feature learning process, which is similar to parameter tuning in other neural network deep learning models. When tuning the ENVINet5 parameters, the model parameter adjustment is friendly. As the module currently supports multi-element target classification, the study mainly focuses on parameter settings such as iteration and training quantity, fixed distance and fuzzy distance, slice sampling ratio, and classification weight and loss weight. Based on our computer hardware environment, the patch size of 256 \(\times\) 256 was set in this study. Considering that there were 125 bands of UHD185 hyperspectral image data after image mosaicking and spectral stitching, and if the 125 bands had been directly set as the number of bands in model initialization, the first convolutional kernel input would have 125 layers, which would seriously affect the patch input rate. Thus, the number of bands after the optimal dimension reduction was selected as the number of bands participating in the training. For the iteration and training quantity-related parameters set in the model training, the number of epochs was set to 25, the number of patches per epoch was set to 200, the number of patches per batch was set to 4, and the patch sampling rate was set to 16.