# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 3 Results
### Results Analysis  
In this study, the Canny operator was used to complete the detection of the guilty range on high-resolution satellite images, and the extraction results were superimposed on the image data, as shown in Figure 8. Based on the DEM data, the regional growing algorithm was used to complete the extraction of positive and negative terrain for each sample area. This study chose peaks and outlets as P-N terrain growing points, respectively. The peak and outlet points could be detected through neighborhood analysis and the watershed boundary method. In the process of growing, the same values of pixels were merged until there were no points to merge. The generated results were also superimposed on hillshade data, as shown in Figure 9. Overall, compared with high-resolution satellite images and hillshade data, the extraction results of edge detection and the regional growing algorithm were better. To compare the performance of the regional growing and edge detection methods for the extraction of the shoulder line, the real shoulder line needed to be defined. In this paper, manual visual interpretation supported by expert knowledge was employed to extract the real shoulder line. The shoulder line extracted using the manual visual interpretation method based on high-resolution satellite images was used as the evaluation criterion. The extraction results of the regional growing and edge detection methods were evaluated by comparing the negative terrain areas of these two methods with the real shoulder line results. It can be seen from Table 2 that, for the four sample areas, the regional growing algorithm was generally better than the edge detection method when the negative terrain  
Figure 8: The result of extracting Negative terrain by edge detection method. (**a–d**) represent 4 sample areas. Figure 9: The results of extracting P-N terrain using the regional growing algorithm: (**a–d**) represent 4 sample areas. areas were used as the evaluation indicator. For example, sample area a, the difference between the results obtained by the regional growing algorithm and the standard results was within 12 ha, and the error was within 5%; the difference between the results obtained by the edge detection method and the standard results was within 41 ha, and the error was within 13%. These results also showed that the results of shoulder line extraction are credible when studying the characteristic indicators related to area, such as gully erosion. However, in the details, we can find that there were still differences between the two methods. The specific analysis was as follows. It can be found from Figure 10 that the regional growing algorithm was more advantageous in the detailed expression of the shoulder line, such as the turning point of the shoulder line. However, it can be found from Figure 11 that, although the edge detection method could reflect more details, the extraction results were fragmented, resulting in inaccurate positioning of the shoulder line in some places, and the generated shoulder line was discontinuous. Although DEM data do not contain more information than high-resolution satellite images, they become an advantage for the positive and negative terrain generated by the regional growing method. This is because the core of the regional growing algorithm is the determination of the growing point. We could accurately find the lowest point (the outlet) and the highest point (the peak) as the positive and negative topographic growth points based on the DEM data using the digital terrain analysis method. Furthermore, for shoulder line continuity obtained through extraction, the regional growing algorithm had more advantages.