# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 3 Method
### Segmentation of Power Lines and Pylons
#### 3.3.1 Power Line Extraction  
According to the abovementioned features of the geometric spatial distribution of overhead PLs, they were extracted in two steps: local line segment extraction based on dimensional features, and global PL merging based on local collinearity and height approximation. The key was to search for linear and parallel structures exceeding the minimum height interval in clustered point sets.  
**Local segmentation**: The local segment extraction was carried out by judging the dimensionality of clustered point set--whether it had a linear feature. The next step was to search for linear structures among neighbors by eigenvalues, i.e., a neighborhood cluster search. The retained results, including linear structure, were seen as candidate PL segments. To visually demonstrate the effectiveness of the algorithm, Figure 5 shows the segmentation result of part of the input data. The resulting linear segments were then pushed to the memory to extract PLs globally.  
Figure 4: Height distribution of points in a block of the transmission corridor: (**a**) height difference between PLs and other objects in the normalized data; (**b**) height histogram of the point cloud where the vertical axis is the number of points.  
PL segments detected within a cluster of voxel blocks were recognized as parts of different PLs. As shown in Figure 5, they did not overlap by local linearity analysis. By this property, a randomly selected seed segment was clustered with segments in neighbor blocks. Each segment was independent of the others, and the calculation in each span was independent. To improve the computation efficiency, this part can run in parallel.  
**Global extraction:** After extracting the candidate PL segments, as shown in Figure 5c, the local collinearity of the adjacent PL segments was analyzed to get complete lines. The whole PL extraction based on local collinearity was achieved by point cloud clustering in the horizontal domain, where the cluster unit was the PL segment, and the constraint was spatial continuity and local collinearity. Meanwhile, a CLF (compass line filter) [1, 68] was applied to analyze the line direction using PL candidate points.  
In the process of clustering, each 2D grid calculated above was set as the center unit, and the PL segments within its eight neighborhoods were searched to merge with seed PL segments located in the center grid. For linear segments aligned with the principle direction of the seed line segment, collinearity with the seed segment was assessed to eliminate those belonging to other, adjacent PLs. Figure 5d shows the extraction results within a span.  
Shown in Figure 5e, the detected PLs may include low-voltage transmission wires that cross underneath. Cross-over or short-spanned low-voltage conductors were detectable, but were not the object of interest. Further optimization by combining with HV tower extraction is detailed in Section 3.4.  
Figure 5: PL extraction: (**a**) 3D grid clustering results of the data in Figure 3a (colored by individual cluster); (**b**) PL segments segmented by height distance (colored by height); (**c**) PL segments extracted by dimensional feature (colored by grids); (**d**) The clustering result of individual lines within a span (colored by individual line); (**e**) PL extraction based on local collinearity, and two examples of extracted PLs including both high-voltage (HV) and low-voltage PLs (colored by height).