## 4 Experiment
### The Second Step CFAR Detection Results  
In the second step of CFAR target detection, we utilize a global two-parameter CFAR algorithm to detect and generate more precise target points from the masks applied to the extracted data, which results in the initial extraction of 3D point clouds illustrated in Figure 14. To validate the results, Figure 15a-c presents a comparison between 3D point, optical image and LiDAR point clouds of the porch structure of the Boyuan Building. Meanwhile, the red boxes in all three subfigures mark the porch structure of the Boyuan Building.  
Figure 14: 3D target point cloud image.  
Figure 12: Three-view masks: (**a**) front-mask; (**b**) top-mask; (**c**) left-mask.  
Figure 13: Potential target point cloud image.  
The laser detection and ranging (LiDAR) data in this paper are the external truth data. LiDAR is an active remote sensing technology that can generate accurate and high density 3D point cloud data. It can be used for generating high precision 3D models. Here, we use it to verify the accuracy of the method-extracted 3D structure of the porch structure of the Boyuan Building. In practice, we compare the length and height of the porch between LiDAR and the proposed method. The experimental truth value comparison table is shown in Table 2. We know from the experiment that the length of the porch structure in LiDAR point clouds is 10 m and the height is 28 m. The length of the porch structure in the detected 3D target point cloud is 11.67 m and the height is 28.4 m. The estimation difference in length and height are 1.67 m and 0.4 m, respectively, which is relatively close [29, 30]. We can conclude both the radar and LiDAR point clouds match the length and height information of the two current porch structures of Boyuan Building as depicted in the optical image. The next step is to applied mDBSCAN to classify the points into cluster, so that the false detections can be reduced.  
To implement the mDBSCAN, three parameters, \(r\), \(h\), and MinPts, should be set. Based on empirical testing, the radius is set to 9 pixels, the cylinder height h is 3 pixels, and the density threshold MinPts is 8. The clustering results are illustrated in Figure 16. Each cluster is labeled with a unique color in the image. The number of all clusters is 22, we can see that the typical structures are all classified, and the noise points are less in the whole data. This figure demonstrates the successful separation of the target area from the remaining strong sidelobe point clusters, achieved through the modified DBSCAN clustering algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Proposed Method (CS-GBSAR)** & **Truth Data (LiDAR)** & **Data Error** \\ \hline Length & 11.67 m & 10 m & 1.67 m \\ \hline Height & 28.4 m & 28 m & 0.4 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of proposed method and truth data.  
Figure 15: Comparison between 3D point cloud and optical image and LiDAR point clouds of the porch structure of the Boyuan Building.  
In Figure 17, we observe distinct 3D spatial distribution characteristics between the target class and the strong sidelobe class in angle, shape, size, and position differences. The biggest cluster is the strong scatter and its sidelobe, which can be seen as a cross structure. The reason for its existence is a staircase from the trihedral-shaped structure as shown in Figure 17a's optical image. Its presence influences the quality of the points extraction results. Based on the angle, shape, size, and position differences, this strong scatter and sidelobe can be removed effectively, and the final extraction results are illustrated in Figure 17b. Then, the precise extraction of the point cloud from the 3D SAR image is achieved.