## 5 Discussion
### Network Structure Analysis  
The entire network of HRNet-CFR is divided into 6 modules: StemNet, Stage1, Stage2, Stage3, Stage4, and CFR. The function for generating low-resolution branches starts at Stage2, Stage3, and Stage4, and the repeated multi-scale fusion function ends at Stage1, Stage2, and Stage3. The network input size is 1024 \(\times\) 1024 \(\times\) 4. We used convolution kernel sizes of 3 \(\times\) 3 and 1 \(\times\) 1, and the Rectified Linear Unit (ReLU) activation function to reduce the nonlinearity in the convolution process and prevent overfitting.  
Table 3 presents the structure of the HRNet-CFR, where \(k\) is the size of the convolution kernel, \(s\) is the convolution step size, and \(p\) is the image padding size before the convolution operation. Compared with ResNet50, the network has more layers, and it has a higher extraction efficiency than DenseNet and ResNet-101.  
Figure 14 shows the visual description of the feature map extraction process of the cropland image to be extracted by the HRNet-CFR model. Figure 14a is a true-color image containing cropland objects in the middle of June, and Figure 14b is a 720-dimensional feature map obtained through the backbone network from Stage4. Figure 14c is a 512-dimensional feature map enhanced by the joint context information CFR. Figure 14f is the probability heat map of the cropland category. Since the CFR module pays more attention to the features of the context, the features of the pixel neighborhood were considered. Figure 14d,e compare the feature maps obtained before and after adding the CFR module, which are easier to capture linear features, such as the edges of roads. Our network uses different scales to extract features, can focus on local features at different resolutions, and can well-separate the boundaries of different types of objects. It can be seen that the network  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Module** & **Convolution** & **Kernels’ Number** & **Convolution Parameter** & **Input** & **Output** \\ \hline \multirow{3}{*}{Stem Net} & Conv1 & 64 & \(k\): 3 \(\times\) 3, \(s\): 2, \(p\): 1 & 1024 \(\times\) 1024 \(\times\) 4 & 512 \(\times\) 512 \(\times\) 64 \\  & Conv2 & 64 & \(k\): 3 \(\times\) 3, \(s\): 2, \(p\): 1 & 512 \(\times\) 512 \(\times\) 64 & 256 \(\times\) 256 \(\times\) 256 \\ \hline \multirow{3}{*}{Stage1} & Conv & 256 & \(k\): 1 \(\times\) 1, \(s\): 1 & 256 \(\times\) 256 \(\times\) 256 & 256 \(\times\) 256 \(\times\) 256 \\ \hline \multirow{3}{*}{Stage2} & Conv & 48 & \(k\): 3 \(\times\) 3, \(s\): 1, \(p\): 1 & 256 \(\times\) 256 \(\times\) 256 & 256 \(\times\) 256 \(\times\) 48 \\  & & 96 & \(k\): 3 \(\times\) 3, \(s\): 2, \(p\): 1 & 256 \(\times\) 256 \(\times\) 48 & 128 \(\times\) 128 \(\times\) 96 \\ \hline \multirow{3}{*}{Stage3} & Conv & 48 & \(k\): 3 \(\times\) 3, \(s\): 1, \(p\): 1 & 256 \(\times\) 256 \(\times\) 48 & 256 \(\times\) 256 \(\times\) 48 \\  & & 96 & \(k\): 3 \(\times\) 3, \(s\): 1, \(p\): 1 & 128 \(\times\) 128 \(\times\) 96 & 128 \(\times\) 128 \(\times\) 96 \\is highly sensitive to the spectrum and texture features of cropland parcels, and can be well-distinguished from the surrounding forest land.