## 4 Results
### Ablation Experiment  
In  
and 0.088, respectively. It shows that the introduced extreme offset strategy can effectively enhance the robustness of the deep network. In addition, Table 4 compares the effect of using extreme offset or center offset on the segmentation accuracy of the proposed method. When extreme offset is adopted, the average F-score of the proposed method is 0.75, which is much higher than the average F-score of center offset (0.43). It shows the effectiveness and superiority of extreme offset. (2) Dynamic bandwidth  
Mean shift with dynamic bandwidth and HAIS clustering with fixed bandwidth are used, respectively, and the segmentation results of the proposed method are shown in Figure 10. After many experiments, when the HAIS clustering with fixed bandwidth is set to 0.05, the segmentation result is the best, and the average F-score of the four plots is 0.78, 0.75, 0.78, and 0.70, respectively. When the mean shift with dynamic bandwidth is used, the average F-score of the algorithm in the four plots reaches 0.85, 0.90, 0.84, and 0.84, respectively, which is significantly higher than the HAIS clustering with fixed bandwidth, as shown in Table 5. The results show that the mean shift with dynamic bandwidth is effective and superior. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Plots** & **RCD** & **TN 1** & **MT 2** & **OE 3** & **CE 4** & \(p\) & \(r\) & \(F\) \\ \hline \multirow{4}{*}{Center offset} & U1 & 61.58\% & 132 & 51 & 52 & 28 & 0.64557 & 0.49515 & 0.56044 \\  & U2 & 69.09\% & 186 & 57 & 75 & 91 & 0.38514 & 0.43182 & 0.40714 \\  & G1 & 93.25\% & 108 & 26 & 56 & 18 & 0.59091 & 0.31707 & 0.41270 \\  & G2 & 96.40\% & 261 & 62 & 159 & 50 & 0.55357 & 0.28054 & 0.37237 \\  & Overall & & 687 & 196 & 342 & 187 & 0.54379 & 0.38114 & 0.43816 \\ \hline \multirow{4}{*}{Extreme offset} & U1 & 61.58\% & 132 & 86 & 19 & 27 & 0.76106 & 0.81905 & 0.78899 \\  & U2 & 69.09\% & 186 & 112 & 58 & 16 & 0.87500 & 0.65882 & 0.75168 \\ \cline{1-1}  & G1 & 93.25\% & 108 & 63 & 18 & 17 & 0.78750 & 0.77778 & 0.78261 \\ \cline{1-1}  & G2 & 96.40\% & 261 & 142 & 74 & 44 & 0.76344 & 0.65741 & 0.70647 \\ \cline{1-1}  & Overall & & 687 & 403 & 169 & 104 & 0.79675 & 0.72826 & 0.75743 \\ \hline \hline \end{tabular}  
* The actual number of individual trees in the plot. 2 The number of correctly segmented individual trees. 3 The number of under-segmented individual trees. 4 The number of over-segmented individual trees.  
\end{table} 
Table 4: Extreme offset vs. Center offset. Figure 9: The curve of loss value and epoch when training and validation are performed in different offset ways. (**a**) training loss and epoch; (**b**) validation loss and epoch. (3) ITS set aggregation  
Table 6 and Figure 11 show that after the introduction of ITS set aggregation, the segmentation accuracy of the proposed method for the four plots is effectively improved, the F-score in the four plots reach 0.86, 0.83, 0.80, and 0.82, respectively, the average F-score in all plots reach 0.83. In the coniferous forest plots (U1, U2) and mixed forest plots (G1, G2), the average F-score is improved by 16% and 14%, respectively, compared with the HAIS set aggregation.