# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 3 Experiment and Results
### Experimental Results and Analysis
During the third set of test samples, we increased the complexity of the scene and the difficulty of processing the data. As part of the third set of data, tall light poles and trees with heights near the tower are also included, increasing the difficulty of segmenting and extracting cableways. The results of the data processing for the third group of samples are shown in Figure 6a-d. Here are the front and side views of the third group of sample point clouds before data processing is performed, as shown in Figure 6a. This set of data contains a number of elements, including light poles and trees. Following data preprocessing, Figure 6b illustrates the front and side views of the data. There are still some light poles, towers, and vegetation point clouds that require further processing in the processed data. The Figure 6c shows the front and side views of the cableway point cloud obtained after segmentation and extraction. By this point, the ropeway segmentation and extraction of the third group of test samples have been completed. A comparison between the ropeway point cloud (white) and the non-ropeway point cloud (color) can be seen in Figure 6d. Performing ropeway segmentation and extraction on the third group of test samples demonstrates that the method proposed in this paper still performs at a high level. Figure 5: The front and side of the second set of sample point clouds (order from left to right): (**a**) before segmentation and extraction; (**b**) after preprocessing; (**c**) following segmentation and extraction; (**d**) displaying the results of the comparison (marked as white and color). Figure 6: Views of the third group of sample point clouds from the front and side (order from left to right): (**a**) before segmentation and extraction; (**b**) after preprocessing; (**c**) after segmentation and extraction; (**d**) the comparison display (marked in white and colored). Next, the data will be compared with the cableway point cloud (considered to be the real value) extracted manually in order to quantitatively examine the extraction effect of the proposed method on the cableway point cloud in the three groups of test samples. As shown in Table 3 and Figure 7, the results of the data comparison are presented. Three groups of sample point cloud data sets are processed using the ropeway segmentation and extraction method presented in this paper; the results of each group are compared with the real values in turn so that the accuracy of data extraction can be evaluated. Additionally, the extraction accuracy of the four cables in the three groups of test samples was compared as part of the comparison process. Table 3 and Figure 7 show that the extraction accuracy rate for each cable in the first group of test samples exceeds 90%, while the overall extraction rate reaches 89.81%; In the second group of samples, each cable's accuracy rate was maintained above 85%, and the overall accuracy rate was 91.54%; Other than the first cable extraction accuracy rate of 81.86%, all other lines in the third group of samples have accuracy rates above 88%, and the overall accuracy rate is 90.43%. Accordingly, the effective extraction rate of this algorithm can reach 90.59%, maintaining a high level of extraction accuracy. In order to further verify the effectiveness of the method proposed in this paper, we conducted cable extraction tests on five groups of samples sequentially using RF, GBDT, SVM, VBF-Net, and the proposed method. The extraction accuracy and extraction time of the test results are presented in Tables 4 and 5, respectively (b