## 5 Discussion
### Potential and Limitations for Cropland Extraction  
From the evaluation results of the two datasets (Jilin-1 and GID), the HRNet-CFR framework provides a good direction to extract cropland, and there is still certain room for accuracy improvement. As mentioned in the Introduction Section, there are currently three main issues affecting the accuracy of automatic extraction of cropland from high-resolution remote sensing images, namely seasonal, heterogeneity, and regional. To reduce the impact of seasonality, we selected remote sensing images of the growing season of crops. In addition to this, sample increments were used to simulate samples in various situations. In the analysis results of different seasons in the Jilin-1 dataset, our method still had advantages. Since the rice is mainly transplanted in water in spring, the features on the image are between water and vegetation. Inevitably, the accuracy also brings a certain decline, especially the accuracy of paddy fields, which dropped by 13% compared to dry land. [PERSON]'s research [59] shows that the time series Sentinel-1 SAR backscattering coefficient feature analysis is very helpful for extracting paddy rice areas. The input of the HRNet-CFR has the potential of multiple data input. In order to improve the extraction \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Image Test Size**} & \multicolumn{3}{c}{**IoU**} & \multirow{2}{*}{**OA**} & \multirow{2}{*}{**Inference**} \\ \cline{3-3} \cline{5-6}  & & **Cropland** & & & & **Time/s** \\ \hline Deeplabv3+ & & 0.777 & 0.812 & 0.795 & 88.64\% & 1.27 \\ UPerNet & & 0.753 & 0.806 & 0.779 & 86.91\% & 1.12 \\ HRNet-CFR & & 0.810 & 0.833 & 0.821 & 90.22\% & **1.01** \\ HRNet-CFR+ & & **0.824** & **0.852** & **0.838** & **92.03\%** & 1.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of different methods compared in the GID dataset. Note: the records in bold represent the best on the GID dataset. accuracy of paddy field boundaries with strong time correlation, we will consider adding time series radar data as auxiliary features to the network to fuse our network. The influence of heterogeneity mainly relies on the network to learn more features to improve the distinguishing ability of the classifier. Compared with other networks [60; 61], our network considers more texture and context features of crops in the CFR module. In the network structure analysis, it can also be seen that the bare land or the forest land next to the road can be well-distinguished. To further improve the potential and capability of the network to separate different agricultural classes, the input side of our network considers that different data can be fused as additional features, such as vegetation index features as well as soil data, to assist in more refined crop information extraction. Meanwhile, we are planning to extend our method to automatically remove mislabeled samples from the training dataset with weak priors. Regarding the difficulty of learning with unbalanced crop sample categories, we will adjust the weight ratio of each class in the network loss function according to the distribution number of each class in the training set image, meaning complex crop samples and samples with less distribution will have more weight. Due to the differences of cropland caused by regional geographical distribution, this is the main aspect to limit the large-scale (province or state) application of the model. The HRNet-CFR model was directly used in a county-sized area (Qingyun county, Shandong Province). From the official area statistics, our extraction error was about 7.9%. In order to make the model of this paper extend to a larger area, the selection of a reasonable target area sample dataset is a factor that has to be considered. Therefore, establishing a reasonable sampling method [62], small-sample learning [63], or a more advanced model transfer learning algorithm [64] can be used for a more in-depth exploration.