## 4 Experiment and Result
### Evaluation of Results
#### 4.4.3 Regional Cropland Extraction  
Compared with natural images, the high-resolution mosaic image has huge data magnitudes. The size of an orthorectified Jilin-1 image is more than 30,000 \(\times\) 30,000 pixels, which greatly exceeds the 512 \(\times\) 512 or 1024 \(\times\) 1024 size of patches used in the training process. As mentioned in the previous section, the sufficiency of contextual information affects segmentation performance, which suggests that pixels close to the edge cannot be classified as confidently as ones close to the center, because the lack of information outside the patch limits the contextual information of the edge region. There may appear to be stitching traces if a regular grid is adopted for cropping, predicting, and mosaicking. To further improve the segmentation performance and obtain smooth masks, we adopted a smoothing strategy with overlapping moving sliding windows, as in the following formula. We counted the number of overlaps in each block in the entire image, superimposed the predicted probability map, and divided by the number of overlaps to obtain the average category probability data of the overlapped part. The extracted result map of a mosaic image obtained in this way greatly reduces the edge effect caused by block division. \[P_{f}=\sum\limits_{i}^{m_{0}}P_{i}^{0}+\frac{\sum\limits_{i}^{m_{1}}P_{i}^{1}} {2}+\frac{\sum\limits_{i}^{m_{3}}P_{i}^{3}}{4} \tag{8}\]  
where \(P_{f}\) is the probability of the entire image obtained according to our strategy, \(P_{i}^{0}\), \(P_{i}^{1}\), and \(P_{i}^{3}\) are cropped patch blocks with 0, 1, and 3 overlaps, respectively, and \(m_{0}\), \(m_{1}\), and \(m_{3}\) are the indexes of the cropped patch blocks whose overlap times are 0, 1, and 3, respectively. As shown in Figure 12, the actual prediction result of the cropped patch has a size of \(I\), and the overlap size is \(a\), \(\frac{1}{8}\) of the \(I\). We calculated the number of overlaps, where the white box is no overlapping area, the blue diagonal striped frame is the area overlapped 1 time, and the red striped box is the area overlapped 3 times. We used the HRNet-CFR model to conduct a regional cropland extraction experiment in Qingyun County, Dezhou City, Shandong Province, a city in northern of China. The image source is a Jilin-1 KF image, the shooting date was 10 May 2020, and the size was 67,466 \(\times\) 44,010 pixels. Its grain crops are mainly wheat and corn. Wheat was mainly sown around the beginning of October last year and harvested around the first ten days of June. Corn sowing is mainly in early April and harvested in early September. At this time, the cropland in the image is mainly wheat. As shown in Figure 13, the area is dominated by \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Number of**} & \multicolumn{4}{c}{**IoU**} & \multirow{2}{*}{**OAs**} \\ \cline{3-3} \cline{6-9}  & & & **Bands** & & **Dry Land** & & **Paddy Field** & **Others** & **Mean** \\ \hline \multirow{4}{*}{ChangChun} & \multirow{4}{*}{HRNet} & March–April & 4 & 0.898 & 0.746 & 0.592 & 0.746 & 91.15\% \\  & & March–April & 3 & 0.877 & 0.763 & 0.669 & 0.770 & 90.14\% \\  & & June–July & 4 & 0.940 & 0.770 & 0.659 & 0.789 & 94.49\% \\ \cline{1-1}  & & March–April & 3 & 0.892 & 0.784 & **0.700** & 0.758 & 91.34\% \\ \cline{1-1} \cline{2-9}  & HRNet-CFR & June–July & 4 & **0.941** & 0.817 & 0.627 & 0.795 & **94.58\%** \\ \hline BaiCheng & HRNet-CFR & June–July & 4 & 0.926 & **0.856** & 0.686 & **0.822** & 93.61\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy evaluation of cropland extraction from multi-region and multi-temporal high-resolution remote sensing images by Jilin-1. Note: the best performance is marked as bold. dry land. The forest land around the city and in the cropland can be better distinguished from Figure 13c.