# A New Method of Ski Tracks Extraction Based on Laser Intensity Information
## 4 Results and Discussion
Analyzing the above results of laser point cloud segmentation based on the intensity distribution of LiDAR point cloud can easily distinguish the snow-covered ground from the non-ground points, but it is still unable to distinguish the ski tracks from the snow-covered ground, so further segmentation operation is needed. Therefore, to further separate the surface, the user-defined parameters of the cloth algorithm were set separately. After several adjustments, the best results can be obtained when cloth resolution is set to 0.2, max iterations is set to 500, and the classification threshold is set to 0.5. The point cloud segmentation results are shown in the Figures 23-26. Figure 21: Selecting a range of intensity values of of the laser point clouds of Figure 20 for segmentation. Figure 22: The point clouds of the Figure 20 is segmented according to the intensity: (**a**) is the non-ground points; (**b**) is the ground points. The included angle between the normal of the seed point field and the normal of the current seed point is calculated and compared with the smoothing threshold. After several attempts, when the smoothing threshold is set to 2.4 and the curvature is set to 0.2, the ski tracks can be separated from other ground points successfully. The results are shown in the Figures 27-30. Figure 7:0 The segmentation results of the point clouds of Figure 22b with \(CSF\): (**a**) is the removed points; (**b**) is the remaining points. Figure 7:1 The segmentation results of the datasets of Figure 16b with \(CSF\): (**a**) is the removed points; (**b**) is the remaining points. For further verification of the effectiveness, this paper compares the method presented in this paper with the Euclidean distance method, the cluster segmentation method, and  
Figure 7:2 The ski-track was extracted from the Figure 24**b**: (**a**) is non-ski-track points; (**b**) is ski-track points. Figure 7:3 The ski-track was extracted from the Figure 26**b**: (**a**) is non-ski-track points; (**b**) is ski-track points. Figure 7:4 The ski-track was extracted from the Figure 23**b**: (**a**) is non-ski-track points; (**b**) is ski-track points. Figure 7:5 The ski-track was extracted from the Figure 25**b**: (**a**) is non-ski-track points; (**b**) is ski-track points. the RANSAC method on three aspects: correct rate, oversegmentation rate, and undersegmentation rate. From the above four groups of test data, snow tracks were extracted using different methods, and the specific comparison results are shown in Figure 7:6  
Taking into account the analysis of Table 1, the extraction accuracy rate of the method proposed in this paper is the highest (92.2%), while the under-extraction rate is the lowest (3.1%), whereas the correct extraction rate of the European distance method is the lowest (68.3%), and the over-extraction rate is the highest (20.4%). As for the second group of experimental data, the extraction accuracy of the proposed method is highest (94.5%), and the under-extraction rate is lowest (2.7%), whereas the clustering segmentation method has the lowest correct extraction rate (74.3%), and the over-extraction rate is highest (12.6%). With the third group of experimental data, the extraction accuracy of most methods has decreased due to the complex test data environment; however, the extraction accuracy of the methods presented in this work is still the highest (89.4%), and the under extraction rate is the lowest (3.8%), whereas the European clustering method has the lowest correct extraction rate (59.8%) and the highest over extraction rate (26.3%). As evidenced by the fourth set of experimental data, the proposed method had the highest extraction accuracy (92.9%) and the lowest under-extraction rate (2.0%), while the clustering segmentation method had the lowest extraction accuracy (70.8%) and the highest over-extraction rate (18.5%).