# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 4 Discussion
### Effects of Different Resolutions on parcel Extraction  
As shown in Figure 13a, due to the different conditions of precipitation, topography, humanity, and climate in each region, there are usually obvious differences in their farmland landscapes. For example, in southwest China, there are mostly hills, so the cultivated land parcels are relatively fragmented, while in northeastern China, large cultivated land is in the majority. Therefore, for different farmland landscapes, different-resolution images  
Figure 12: Comparison of parcels extracted from different temporal images in part of the A2 area (The red and white circles mark the significant differences in the parcel extraction results).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Date** & \(\mathbf{P}\) & \(\Delta\) & \(\mathbf{R}\) & \(\Delta\) & \(\mathbf{F1}\) & \(\Delta\) & \(\mathbf{IoU}\) & \(\Delta\) \\ \hline
6.11 & 0.87 & \(-\)0.02 & 0.74 & \(-\)0.09 & 0.8 & \(-\)0.04 & 0.67 & \(-\)0.05 \\
8.10 & 0.88 & \(-\)0.01 & 0.7 & \(-\)0.13 & 0.78 & \(-\)0.06 & 0.64 & \(-\)0.08 \\
9.9 & 0.88 & \(-\)0.01 & 0.76 & \(-\)0.07 & 0.81 & \(-\)0.03 & 0.69 & \(-\)0.03 \\
9.26 & 0.87 & \(-\)0.02 & 0.73 & \(-\)0.1 & 0.79 & \(-\)0.05 & 0.66 & \(-\)0.06 \\
6.11 + 8.10 + 9.9 + 9.26 & 0.89 & - & 0.83 & - & 0.84 & - & 0.72 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of accuracy performance of parcel extraction for different temporal images.  
should be used for parcel extraction to achieve the optimal extraction accuracy and the highest extraction efficiency.  
The study area in this research also contains complex agricultural landscapes, ranging from A1, which is flat and mainly planted with corn and regular parcels, to A2, which is predominantly hilly and has broken parcels; and in Youyi County, there are also large-area parcels planted with corn and soybeans as well as small-area parcels planted with rice. From the results of the study, it can be seen that the 10 m images can well extract the large parcels but cannot precisely recognize the small parcels, so there is an obvious difference in the extraction accuracy in different study areas. For the small-area rice parcels in Youyi County, this study used GF-7 2.65 m multispectral data and 0.65 m panchromatic data to perform the extraction comparison (Figure 13b), and found that the extraction result of 0.65 m resolution was significantly better than that of 2.65 m, which proves that for small-area rice parcels, sub-meter scale imagery is more useful. In this study, the effect of resolution on parcel extraction was only qualitatively analyzed in Northeast China. However, as shown in Figure 13a, more diverse geographical conditions exist. Therefore, when evaluating the performance of parcel extraction model in these complex areas, data quality, parcel shape, and boundary precision are equally critical. In future work, we will leverage richer datasets and improved experiments to quantitatively determine the optimal resolution for parcel extraction across diverse farmland landscapes. Additionally, we plan to explore hybrid approaches and constraint-guided segmentation to address SAM's limitations in complex parcels, such as integrating semantic information with shape and boundary constraints to improve extraction accuracy in fragmented and irregular agricultural areas.  
Figure 13: (**a**) Differences in parcels between different agricultural landscapes. (**b**) Comparison of rice parcel extraction performance under different resolutions in Youyi County.