# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 4 Discussion
### Applications and Future Research  
The landform types of the Loess Plateau in northern Shaanxi show significant regional differences, and the dominant factors of landform influence vary for different landform types. Loess landform types are mainly divided into tableland, ridges, and hills [38; 39; 40], and there were challenges in realizing the fully automatic extraction of the Loess Plateau shoulder line. For the Loess Plateau, it can be seen from the previous accuracy evaluation results that the proposed method had good applicability. To further verify the applicability of this method in extracting shoulder lines, we selected a loess tableland area with an area of 35,975.531 ha. The results are shown in Figure 15. By superimposing the extracted results onto high-precision images, it can be seen that the extracted shoulder lines were  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operator Type** & **Number of Lines** & **Maximum Length (m)** & **Total Length (m)** \\ \hline Prewitt & 427 & 12,116 & 21,033 \\ Sobel & 514 & 6283 & 23,877 \\ Robert’s & 441 & 9867 & 23,196 \\ Laplace & 154 & 17,421 & 22,966 \\ Canny & 87 & 19,308 & 22,393.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of evaluation index values of different edge detection operators.  
Figure 14: (**a**) represents sample area a; (**b–f**) represent the extraction results of the [PERSON], [PERSON], Sobel, Laplace, and Canny operators, respectively.  
effective and reasonable, which verified that this method had good universality in Loess tableland area.  
In loess ridge and hill regions, the terrain was more complex. As shown in Figure 16, there were many seriously discontinuous shoulder lines due to occasional gravity erosion factors, such as landslides and scattering. Some areas were also affected by artificial landforms, such as terraced fields, dams, etc., resulting in the existence of multilevel shoulder lines (Figure 16a). At the same time, in loess ridge and hill regions, due to the influence of vegetation, the slope of the shoulder line's up- and down-slopes showed little change, and there were invisible shoulder lines (Figure 16b,c). Even if there is no interference from vegetation, in autumn and winter the continuity and visibility of shoulder lines are poor due to the impact of gravity, such as landslides, strays, and collapses, or even human factors (Figure 16d). Therefore, the application of this method in loess ridge and hill areas is also a problem that needs to be discussed and solved in the future. In future research, it is necessary to further analyze the applicability of this method for different data sources and different landform types. To better apply the method to different landform types over a large area, we could try to divide different areas according to the different land use characteristics (soil erosion characteristics and land use directions) of each landform type. On this basis, we could establish quantitative models of different shoulder lines to achieve the high-efficiency and high-precision extraction of shoulder lines.  
Figure 15: Shoulder lines extracted by applying the method proposed in this study.