# A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic
## 3 Results and Analysis
### Experimental Results  
Both of the aforementioned datasets undergo identical preprocessing steps, followed by the application of the SOSG method. The results obtained from the proposed method are then juxtaposed with those from [PERSON] et al. [20] and [PERSON] et al. [25]. The [PERSON] et al. [20]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **OrbitID** & \begin{tabular}{c} **Imaging** \\ **Mode** \\ \end{tabular} & \begin{tabular}{c} **Nominal** \\ **Resolution** \\ \end{tabular} & \
\begin{tabular}{c} **Incidence** \\ **Angle** \\ \end{tabular} &
\begin{tabular}{c} **Imaging** \\ **Date** \\ \end{tabular} \\ \hline \multirow{8}{*}{\(1\)} & 017924 & UFS & 3 m & 32.57\({}^{\circ}\) & 2020-01-05 \\  & 017924 & UFS & 3 m & 32.57\({}^{\circ}\) & 2020-01-05 \\  & 024683 & UFS & 3 m & 26.14\({}^{\circ}\) & 2021-04-18 \\  & 024683 & UFS & 3 m & 26.14\({}^{\circ}\) & 2021-04-18 \\  & 024683 & UFS & 3 m & 26.14\({}^{\circ}\) & 2021-04-18 \\  & 017160 & UFS & 3 m & 27.28\({}^{\circ}\) & 2019-11-13 \\  & 017160 & UFS & 3 m & 27.28\({}^{\circ}\) & 2019-11-13 \\  & 017160 & UFS & 3 m & 27.28\({}^{\circ}\) & 2019-11-13 \\  & 022594 & UFS & 3 m & 28.40\({}^{\circ}\) & 2020-11-24 \\  & 022594 & UFS & 3 m & 28.40\({}^{\circ}\) & 2020-11-24 \\ \hline \multirow{8}{*}{\(2\)} & 032539 & UFS & 3 m & 33.54\({}^{\circ}\) & 2022-10-15 \\  & 032539 & UFS & 3 m & 33.54\({}^{\circ}\) & 2022-10-15 \\  & 032539 & UFS & 3 m & 33.54\({}^{\circ}\) & 2022-10-15 \\  & 032539 & UFS & 3 m & 33.54\({}^{\circ}\) & 2022-10-15 \\  & 032558 & UFS & 3 m & 34.49\({}^{\circ}\) & 2022-11-13 \\  & 032598 & UFS & 3 m & 34.49\({}^{\circ}\) & 2022-11-13 \\  & 03258 & UFS & 3 m & 34.49\({}^{\circ}\) & 2022-11-13 \\  & 03258 & UFS & 3 m & 34.49\({}^{\circ}\) & 2022-11-13 \\  & 032366 & UFS & 3 m & 31.57\({}^{\circ}\) & 2022-10-03 \\  & 032366 & UFS & 3 m & 31.57\({}^{\circ}\) & 2022-10-03 \\  & 032366 & UFS & 3 m & 31.57\({}^{\circ}\) & 2022-10-03 \\  & 032366 & UFS & 3 m & 31.57\({}^{\circ}\) & 2022-10-03 \\  & 033266 & UFS & 3 m & 31.57\({}^{\circ}\) & 2022-10-03 \\  & 033266 & UFS & 3 m & 31.57\({}^{\circ}\) & 2022-10-03 \\  & 033375 & UFS & 3 m & 36.30\({}^{\circ}\) & 2022-12-12 \\  & 033375 & UFS & 3 m & 36.30\({}^{\circ}\) & 2022-12-12 \\  & 033375 & UFS & 3 m & 36.30\({}^{\circ}\) & 2022-12-12 \\  & 033375 & UFS & 3 m & 36.30\({}^{\circ}\) & 2022-12-12 \\  & 0332611 & UFS & 3 m & 29.48\({}^{\circ}\) & 2022-12-12 \\  & 032611 & UFS & 3 m & 29.48\({}^{\circ}\) & 2022-10-20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information of two datasets.  
Figure 4: Geometric distribution of two datasets. (**a**) Dataset 1. (**b**) Dataset 2.  
method is pixel-based, utilizing pixels in the optical image overlapping areas to construct a difference matrix and searching for optimal seamless through the Dijkstra algorithm. On the other hand, [PERSON] et al. (2019) presents a typical object-based method for optical image optimal seamline generation. The experimental results are depicted in Figures 5 and 6, with detailed results showcased in Figures 7 and 8.  
Figure 5: Comparison of results of Dataset 1. (**a**) Initial seamline network and its mosaic result. (**b**) [PERSON] et al. (2019) method result. (**c**) [PERSON] et al. (2019) method result. (**d**) SOSG method result. The red, yellow, blue, and green lines are the seamline network in (**a**), (**b**), (**c**), and (**d**), respectively.  
Figure 6: Comparison of results of Dataset 2. (**a**) Initial seamline network and its mosaic result. (**b**) [PERSON] et al. [20] method result. (**c**) [PERSON] et al. [25] method result. (**d**) SOSG method result. The red, yellow, blue, and green lines are the seamline network in (**a**), (**b**), (**c**), and (**d**), respectively.