## 3 Method
### Proposed Method  
Figure 5 shows the Flow chart of two-step CFAR CS-GBSAR 3D point cloud extraction method. The method includes the following basic steps. It first utilizes a maximum projection to obtain three-view images from 3D image data. Then, the first step CA-CFAR is applied to obtain the coarse masks of three-views. Then, the volume mask in original 3D image is obtained via inverse projection. This can remove strong sidelobes outside the potential target region and obtain potential target area data by intersecting it with the SAR 3D image. Then, the second step CA-CFAR is applied to potential target area data to obtain 3D point clouds. Finally, to further eliminate the residual strong sidelobes and output accurate 3D point clouds, the modified DBSCAN clustering algorithm is applied. The modified DBSCAN clusters with a cylindrical template to accommodate the data's feature rather than the original spherical template, which can reduce false clustering. Then, it enables accurate extraction of the final 3D target point cloud. While the classical two-parameter CFAR algorithm is capable of detecting target areas in 3D SAR images, it requires detect target layer by layer at different height. The strong sidelobes often hinder accurate detection of target points. Compared with the classical two-parameter CFAR layer by layer operation, this approach mitigates the impact of sidelobes and enables efficient extraction of 3D point clouds even when strong sidelobes are presented. Figure 4: DBSCAN clustering principle diagram. 3D data are \(S(x,y,z)\), the projection left-view \(S_{L}=max(S,x_{0})\), where \(x_{0}\) is the direction vector of X axis; the projection front-view \(S_{F}=max(S,y_{0})\), where \(y_{0}\) is the direction vector of Y axis; the projection top-view \(S_{T}=max(S,z_{0})\), where \(z_{0}\) is the direction vector of the Z axis. As shown in Equation (7). \[ \begin{cases}S_{L}=max(S,x_{0})&\text{left-view}\\ S_{F}=max(S,y_{0})&\text{front-view}\\ S_{T}=max(S,z_{0})&\text{top-view}\end{cases}  \tag{7}\]  
Figure 5: Flow chart of two-step CFAR CS-GBSAR 3D point cloud extraction method. Figure 6: Three-view projection diagram. * The first step CFAR The first step CFAR is to apply conventional CA-CFAR to the three-view image to obtain the three-view mask, as shown in Equation (8). They are left-view mask \(M_{L}(y,z)\), front-view mask \(M_{F}(x,z)\) and top-view mask \(M_{T}(x,y)\). \[ \begin{cases}M_{L}(y,z)=CFAR[S_{L}]&\text{left-view mask}\\ M_{F}(x,z)=CFAR[S_{F}]&\text{front-view mask}\\ M_{T}(x,y)=CFAR[S_{T}]&\text{top-view mask}\end{cases} \] (8) where: \(S_{L}\): is left-view; \(S_{F}\): is front-view; \(S_{T}\): is top-view; \(M_{L}\): is left-view mask; \(M_{F}\): is front-view mask; \(M_{T}\): is top-view mask. By inverse projecting the three-view mask, the volume mask in the original 3D image is obtained, which is subsequently intersected with the SAR 3D image. This can remove strong sidelobes outside the potential target region and obtain potential target area data. Figure 7 depicts the process schematic. In Figures 6 and 7, red, green, and blue correspond to the projections of pixels in the left view, the main view, and the top view, respectively. The purple sphere represents the intersection of projections in all data. Three-view mask inverse projection process can obtain 3D mask \(I(x,y,z)\). The corresponding derived formula is given as Equation (9). \[I(x,y,z)=inverse\ projection[M_{L}\ M_{F}\ M_{T}] \tag{9}\]  
The 3D mask \(I(x,y,z)\) is then intersected with the 3D data \(S(x,y,z)\) to obtain the potential target area data \(f(x,y,z)\). The corresponding derivation as shown in Equation (10). \[f(x,y,z)=S(x,y,z)\cap I(x,y,z) \tag{10}\]  
* The second step CFAR The second step CFAR is initiated to perform global two-parameter CFAR detection on the potential target area data \(f(x,y,z)\). This produces the coarse 3D target point cloud represented by \(P(x,y,z)\).