# A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic
## 3 Results and Analysis
### Visual Assessments
Figure 8(b6) has a wide range of water with many options for the location of the seamlines, in which case the seamlines generated by the [PERSON] et al. [20] method can meet the requirements for usage. Figure 8(c1-c6) are the seamlines generated by the [PERSON] et al. [25] method. The seamline generated by the [PERSON] et al. [25] method in Figure 8(c1) is more similar to the seamline generated by the [PERSON] et al. [20] method, and the decision-making is similar for both methods. Similarly, in the presence of more complex scenarios, the seamline generated by the [PERSON] et al. [25] method is similar to that of the [PERSON] et al. [20] method, as shown in Figure 8(c2). In Figure 8(c3), the [PERSON] et al. [25] method avoids some of the layover areas, but still traverses some of the layover areas, compared to the [PERSON] et al. [20] method. In Figure 8(c4), the decision of the [PERSON] et al. [25] method is to prioritize the crossing of the road, and therefore the seamlines are concentrated towards the road sides. In Figure 8(c5), in the same complex scenario, the decision of the [PERSON] et al. [25] method is to search for the seamline along the road or the boundary between buildings and non-buildings, which is more reflective of the [PERSON] et al. [20] method than the segmentation effect, but still traverses more buildings. In the presence of a large amount of water, the [PERSON] et al. [25] method still has a higher priority for roads, with the seamline preferentially following the road, as shown in Figure 8(c6). Figure 8(d1-d6) show the seamlines generated by the SOSG method. In Figure 8(d1), the SOSG method chooses a river between a road and a river, and the seamline on the right area uses a stream as the basis for searching, because there are fewer buildings on both sides of the seamline, and therefore the overall visual effect is better than the other two methods. The performance of the SOSG method in the case of complex scenarios faced is demonstrated in Figure 8(d2), where the right area has roads and lakes that have a very high level of selection of seamlines in the extraction of consistency-stable parcels, and therefore, in the case of not being able to avoid the complex of buildings, the SOSG method is more advantageous than the other two methods by traversing the least number of buildings and moving to parcels with lower weights for the seamline search. Figure 8(d3) shows the existence of a layover area; in this case, due to the characteristics of the layover area, the consistency-stable parcel extraction process can also judge the layover area as parcels with higher weights, so that the layover area can be avoided. As can be seen from the figure, the parcels are avoided in the face of multiple layover areas, and the parcels can also be selected in the road when the road appears. As shown in Figure 8(d4), the SOSG method has good judgment for homogeneous areas like farmland, generating seamlines in farmland areas while avoiding building clusters as much as possible, and at the same  
time the path is the shortest compared to the other two methods. In complex scenarios, faced with the unavoidable traversal of building complexes, the seamline generated by the SOSG method chooses to prioritize the search along roads, as shown in Figure 8(d5), and the decision is similar to that of the [PERSON] et al. [25] method, but less obvious feature targets than the [PERSON] et al. [25] method traverses fewer obvious feature targets. In Figure 8(d6), the large amount of water provides more choices for seamline detection, and the SOSG method prioritizes searching for seamlines along the water, which is different from the mosaics detected by the [PERSON] et al. [20] method, but the decision making is similar. In this dataset, it is well demonstrated that the SOSG method, guided by consistency-stable parcels, has a better ability to select seamlines, and the location of seamlines is more in line with human intuition.