## 4 Experiment and Result
### Evaluation of Results
#### 4.4.2 Results of Jilin-1 Dataset  
This section is based on the images of the Jilin-1 dataset to evaluate the accuracy of cropland extraction in different regions, different time phases, and different bands in Jilin Province.  
Figure 11 visually analyzes the results of cropland extraction in the central region of Jilin province with small geographic differences, and the western region with large  
Figure 10: The training diagram of the cropland extraction network in GID data: (**a**) the decreasing curve of training loss, (**b**) the learning rate curve, and (**c**) the validation set accuracy curve.  
Figure 9: The training diagram of the cropland extraction network in Changchun city in the spring Jilin-1 data: (**a**) the decreasing curve of training loss, (**b**) the learning rate curve, and (**c**) the validation set accuracy curve.  
differences. Figure 11a,d show the western regions with large geographic differences. From the visualization result, it can be seen that there is more saline-alkali land and fragmented land in the Baicheng research area, and there is a certain degree of omission and mis-segmentation. Figure 11i shows the model obtained through training in Changchun city during the period from April to May, and the result of extracting the cropland of Siping city by transferring the previously trained model. It can be seen from the results that the generalization ability of the HRNet-CFR model is relatively strong, and the dry land is basically correctly proposed. Due to the growth of crops in paddy fields with time difference, and since the time correlation is strong, part of dry land is mistakenly segmented as paddy field. Comparing Figure 11h,i, it can be found that the algorithm completely proposes roads that are not marked in the true data.  
From Table 2, showing the results of the cropland extraction in the Changchun study area, it can be found that a four-band (B, G, R, NIR) image is more suitable for extracting cropland. Compared with the three-band (B, G, R) image extraction results, the overall accuracy of the comparison of different bands from March to April improved by 1.01%. Among different types of cropland extraction, June to July is more suitable for distinguishing dry land and paddy land. From the results extracted in March to April, the IoU of dryland and paddy field increased by 4.2% and 2.4%, respectively. The addition of contextual features is helpful for the extraction of cropland. Compared with HRNet, the HRNet-CFR method  
Figure 11: Visualization results of multi-region and multi-temporal cropland extraction in Jilin province: (**a**) true-color image of Songyuan research area in August, (**d**) true-color image of Baicheng research area in July, (**g**) true-color image of Sipin research area in May, (**b,e,h**) ground truth of cropland, and (**c,f,i**) semantic segmentation results of our method.  
had a certain improvement in the extraction accuracy, and the overall accuracy increased by about 1.2%.  
In the Baicheng area, due to the salinization of part of the land [56], there are certain geographical differences, which brings certain challenges to the extraction of cropland. In the extraction results from June to July, the overall accuracy of cropland extraction in Changchun city was about 0.97% higher than that in Baicheng.