## 3 Experimental Results and Analysis
### Experimental Comparison and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Scale** & **Feature Expression** & \begin{tabular}{c} **Post-Processing** \\ **Optimization** \\ \end{tabular}  & **Classifier** \\ \hline \begin{tabular}{c} Our \\ method \\ Method 1 \\ Method 2 \\ Method 3 \\ Method 4 \\ Method 5 \\ Method 6 \\ Method 7 \\ \end{tabular}  & \ \begin{tabular}{c} Multi-scale \\ Multi-scale pyramid \\ Single scale \\ Single scale \\ Single scale \\ Multi-scale scale \\ Multi-scale scale \\ Multi-scale scale \\ Multi-scale scale \\ Multi-scale scale \\ Multi-scale scale \\ Multi-scale scale \\ Multi-scale scale \\ Multi-scale scale \\ \end{tabular}  &
\begin{tabular}{c} \(F_{2}+F_{RAD}+F_{LSH}+F_{cov}+F_{plane}\) \\ Multi-scale pyramid \\ Multi-scale pyramid \\ Multi  
For scene 1, the accuracy of the proposed method is the highest, and the value of the precision/recall is relatively high (the classification result of the proposed method is shown in Figure 1e). From the classification results evaluation of the three kinds of objects by the \(F_{1}\)-score, one can see that the classification effect on cars for the proposed method is not as good as for Method 7. Meanwhile, the tree and building classes can basically be classified correctly. For scene 2, the precision/recall of trees of the proposed method are lower than for Method 7. Meanwhile, the precision/recall of buildings and cars are the maximum compared with other methods. According to the classification result of the \(F_{1}\)-score value, the proposed method has a better effect than the other methods, except that the tree classification performance is worse than for Method 7. Considering the accuracy, precision/recall rate and the \(F_{1}\)-score evaluation comprehensively, the proposed method has a better classification performance than the other methods, and the proposed method has the highest overall accuracy for both scenes. This proves that the proposed point cloud classification method based on point multilevel features is effective, and that it can accurately classify the ALS point cloud data in large scale scenes. In order to more intuitively compare the classification performance of each method, Figure 5 shows the performance of the eight classification methods in scene 2. Figure 5 shows that the proposed method can classify most points correctly. Compared with other comparison methods, the classification accuracy of the proposed method is higher. From the comparison between Figures 5c-e and 5b, the classification effect of the proposed method is obviously better than that of the other three algorithms, especially in the buildings and trees. It can be seen from the comparison between Figure 5f,g and Figure 5b that Method 4 and Method 5 have more misclassifications for cars and buildings, and that the performance of the proposed method is significantly better than that of the other two methods. In comparing Figure 5h,i with Figure 5b, one can see that Method 6 and Method 7 have a similar classification performance to that of the proposed method. However, a certain number of architectural edge points are classified incorrectly, and the top part of the trees is also classified incorrectly. One can see from the comparison between Figures 5f-i and 5b that the compared methods have some misclassifications for the edge points and for two objects that are overlapping regions. However, the proposed method has fewer misclassifications in those regions than for the other methods. This proves that the proposed feature descriptors and post-optimization strategies can improve the classification results.