## 5 Discussion
### Comparison with the Results of Traditional Methods  
This section presents the method comparison experiment to demonstrate the necessity of the proposed method, i.e., the classical two-parameter CFAR layer-by-layer detection algorithm and the two-step CFAR detection algorithm. Subsequently, both the traditional DBSCAN clustering algorithm and the modified DBSCAN clustering algorithm were applied to extract the point cloud and suppress false alarms. The comparison results of the resulting 3D initial point cloud views are depicted in Figure 18. Figure 18a is the 3D initial point cloud extracted by two-parameter CFAR layer-by-layer detection, and (b) is the 3D initial point cloud extracted by two-step CFAR detection. The image obtained via the two-step CFAR algorithm preserves the target area with significantly reduced sidelobes, resulting in a clearer extraction. While the conventional method has more clutters remain in each layer. The parameters of the conventional method are optimized to reach the best performance. To further show the difference, the three-view of the initial extraction result of the 3D target point cloud is shown in Figure 19. Figure 19a-c are the three-view projection of the initial extraction point cloud after the classical two-parameter CFAR target detection algorithm is detected layer by layer. Figure 19d,e are generated by the two-step CFAR algorithm. The main target area is labeled by a red box, and the secondary target is labeled by a blue box. The main target area is the porch structure of the Boyuan Building of North China University of Technology, and the secondary target is the playground fence. Through a comparison of results obtained using the two-parameter CFAR algorithm and the two-step CFAR algorithm, it becomes evident that the classical two-parameter CFAR algorithm produces an abundance of false alarms, which fails to accurately distinguish the target area from the existing detection results, while the proposed method is clearer. Hence, the two-step CFAR algorithm proves more effective for SAR 3D image target detection with complex backgrounds, making it ideal for the initial extraction of 3D imaging point clouds. Figure 20 illustrates the comparison results of 3D point cloud clustering using two different methods: (a) is the traditional DBSCAN clustering result; (b) is the modified DBSCAN clustering result; and (c,d) are two method results from removing the strong sidelobe. Although both the methods classify targets and strong sidelobes into separate clusters, the traditional DBSCAN clustering result contains more noise points, which are wrongly classified. In such condition, it is impossible to accurately cluster and distinguish the target class and the strong sidelobe class, and the false alarm suppression effect is poor. The conventional DBSCAN clustering algorithm noise class contains 1225 points, while the modified DBSCAN clustering algorithm noise class contains 56 points. The comparison experiment shows that the proposed method can reduce 95.4% false clustered points and remove the strong traces, which shows the better performance of the proposed method. The modified DBSCAN algorithm has less noise points in the clustering results. It  
Figure 19: Comparison of 3D target initial point cloud three view projection results. Figure 18: Comparison results of 3D target initial point cloud view results. can distinguish the target from the residual strong sidelobes while maintaining a certain noise reduction ability. It restores the positioning information of each point cluster greatly, making the point cloud extraction results more accurate and the target clearer. In a similar way, the three-view comparison results are demonstrated through 3D point cloud clustering as in Figure 21. Figure 21a-e are three view of point cloud results generated by conventional DBSCAN and a modified DBSCAN algorithm, respectively.