# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 3 Methodology  
### Structure of the Proposed CEM  
The CEM model consists of a feature extractor and classifier; the feature extractor comprises an encoder and decoder (Figure 4), the fused GF-2 image and the corresponding label file were used as input. The band order of the image is red, blue, green, and near-infrared. The encoder is composed of a spectral feature extraction unit and five semantic feature extraction units. In Figure 4, E0 represents the spectral feature extraction unit, and E1, E2, E3, E4, and E5 represent semantic feature extraction units, respectively. The word semantic means \"similar category information\"; in other words, the feature extraction unit extracts similar feature values from pixels belonging to the same category. The spectral feature extraction unit uses 16 1 \(\times\) 1-type convolution kernels, including a fixed convolution kernel and 15 ordinary convolution kernels, capable of extracting 16 features from the spectral information of a pixel. The value of the fixed convolution kernel is represented by the vector [0, 0, 0, x], and only the last component is adjusted during the training process. The purpose of the design is to fully exploit the sensitivity of the near-infrared band to vegetation. The image row and  
Figure 4: Structure of the proposed crop extraction model (CEM) model. ReLU: rectified linear unit; D: Decoder; E: Encoder. Figure 3: Example of image classification: (**a**) original Gaofen 2 image and (**b**) image classified by land use type. the column structure are unaltered by the spectral feature extraction unit. Therefore, we used this structure as the basic feature for the fusion of each feature level. Each semantic feature extraction unit includes three feature extraction layers and one pooling layer. Each feature extraction layer has a convolutional layer, a batch normalization layer, and an activation layer for extracting semantic features. The convolutional layers are comprised of 3 \(\times\) 3-type convolution kernels. Table 1 presents the number of convolution kernels for each convolutional layer. The activation layer uses the rectified linear unit function as the activation function. The pooling layer was used to optimize the features, but because the ordinary \(2\times 2\) pooling kernel reduces the resolution of the image, we adopted a new pooling strategy. As there are a greater number of pixels for the same land use type in the early feature extraction stages, we used a \(2\times 2\) type pooling kernel in the 1 st, 2 nd, and 3 rd feature extraction units. The use of the \(2\times 2\) type pooling kernel potentially accelerates feature aggregation. In the later stages of feature extraction, the resolution differs significantly from that of the original image, so we adjusted the step size to 1 in the 4 th and 5 th units. In the 4 th and 5 th feature extraction units, the size of the pooling kernel is still \(2\times 2\), but when the size of the feature block participating in the pooling operation is smaller than the size of the pooling kernel, the size of the pooling kernel is adjusted to match the feature block; this ensures that valid pooling values are obtained. Therefore, the 4 th and 5 th feature extraction units do not change the size of the feature map. The decoder is also composed of five units, as shown by D1, D2, D3, D4, and D5 in Figure 5. The decoder up-samples the feature map to gradually restore the feature image to the size of the original image. The decoder generates feature vectors with the same structure for each pixel. The D5 and D4 units are composed of convolution layers. The convolution layer, which works in the'same' mode in Tensonflow, is used to adjust feature values without changing the size of the feature map. The D5 unit directly uses the output of the E5 unit as input. The input of the D4 unit is the output of the D5 unit and the output of the E4 unit. The inputs are first fused using Equation (1) and then adjusted using the convolution layer. \[\mathrm{f}=\mathrm{a}*\mathrm{d}5+\mathrm{b}*\mathrm{e}4 \tag{1}\]