## 5 Discussion
### Comparison with Other Methods  
In order to compare the performance of different algorithms, the popular Deeplabv3+ and UPerNet were compared with the HRNet-CFR method based on the public GID dataset. Deeplabv3+ adopts the \"encoding-decoding\" mode. The main body of its encoding is DCNN with hole convolution. This experiment uses ResNet101, and then the spatial pyramid pooling module with hole convolution, mainly to introduce multi-scale information to improve the ability of small-target semantic segmentation. Compared with DeepLabv3, DeepLabv3+ introduces a decoding module, which further merges the low-level features with the high-level features to improve the accuracy of the semantic segmentation boundary. UPerNet is a model based on the feature pyramid network and pyramid pooling module. It trains the strategy to learn and label the complex content of the scene and identify  
Figure 14: Visualization of the feature map of the target of interest in the image by the HRNet-CFR model. **(a)** Original true-color image, **(b)** visualization of the feature in Stage4, **(c)** visualization of the final fusion feature through CFR, **(d)** the visualization of one rich channel before CFR module processing, **(e)** the visualization of one rich channel of the CFR module, and **(f)** the probability heat map of cropland. Note: the features are normalized between 0–1. The redder the color, the more prominent the characteristics of cropland target. The bluer the color, the more prominent the other target features. as many visual concepts as possible from it. In some studies [58] on the segmentation of urban scenes in higher-resolution aerial remote sensing images, it has shown a good segmentation effect. Through the comparison, it was found that the overall accuracy of our approach can reach 92.03%, which is 3.39% higher than Deeplabv3+ and 5.12% higher than UperNet. The visualization results extracted by the two methods are shown in Figure 15.  
Figure 15 shows the comparison of the visual extraction results of Deeplabv3+, UperNet, and the HRNet-CFR under different scenarios in the GID dataset. Among them, the first column of Figure 15 selects paddy fields, dry fields under different growth conditions, arable land with mulching film, and terraced fields, respectively. Figure 15a selects paddy field areas with large heterogeneity. It can be seen from the visualization result in Figure 15e that our model was more sensitive to roads and can divide the cropland completely. The cropland that is not labeled in the ground truth  
Figure 15: Comparison of different semantic segmentation methods in the GID dataset for cropland extraction in different geographic scenarios. (**a,g,m,s**) True-color images of different geographic scenes, (**b,h,n,t**) ground truth labels, (**c,i,o,u**) semantic segmentation results of Deeplabv3+, (**d,j,p,v**) semantic segmentation results of UperNet, (**e,k,q,w**) semantic segmentation results of HRNet-CFR, and (**f,l,r,x**) the morphological post-processing results of HRNet-CFR (the red boxes marked in the last two columns are to better highlight the difference between before and after morphological post-processing). (Figure 15b) could also be better identified. Figure 15c shows the result of Deeplabv3+ cropland extraction. It was observed that the boundary had obvious sawtooth traces, and the extracted boundary of Figure 15e was smoother. Figure 15g,m show the texture and shape of cultivated land in different seasons, and it can be seen that these methods can segment well. In detail, our method segmented the plot more completely in Figure 15k. Regarding regional comparisons, Figure 15u,w illustrate the extraction result that HRNet-CFR can better put forward terrace and distinguish it from the surrounding forest land in Southwest China. Compared with other methods, the UPerNet has poor network generalization and missing segmentation for the southern terrace, which is quite different from the northern cropland.