# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 4 Experiments and Results
### Results of Benchmark Data  
The segmentation results for the two benchmark plots (B1 and B2 in [22]), are presented in Table 6. As the ground truth did not cover all trees in the plots, only the match rates (recall) could be determined. Therefore, no precision and F1-Score are provided. For plot B1, the Sphere kernel produced similar match results to the Cylinder kernel when the kernels were adaptive. However, they were both outperformed by the Pollock kernel when the kernel was set to be fixed. For plot B2, the Sphere kernel generated as good match rate as the Pollock kernel, outperforming the Cylinder kernel. The Pollock kernel performed the best for both plots when it was non-adaptive (fixed), such as for the simulated data. Notice that the bandwidth values which generate better results for the two plots are slightly different. Nevertheless, the Pollock kernels that performed \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Plot** & **Shape** & **Band a** & **Ratio b/a** & **Weight** & **Adapt** & **Match** & **Over Segment** & **Under Segment** \\ \hline \multirow{9}{*}{B1} & Sphere1 & 7 & 1 & N & Y & 0.8393 & 0.0536 & 0.1071 \\  & Sphere2 & 2 & 1 & N & N & 0.7857 & 0.1786 & **0.0357** \\  & Sphere3 & 2 & 1 & Z & N & 0.8214 & 0.125 & 0.0536 \\  & Cylinder1 & 7 & 2 & N & Y & 0.8393 & 0.0536 & 0.1071 \\  & Cylinder2 & 7 & 2 & Z & Y & 0.8393 & 0.0714 & 0.0893 \\  & Cylinder3 & 2 & 2 & N & N & 0.7679 & 0.0893 & 0.1429 \\  & Cylinder4 & 2 & 2 & Z & N & 0.7679 & 0.0179 & 0.12143 \\  & Pollock1 & 6 & 2 & N & Y & 0.8214 & 0.0357 & 0.1429 \\  & Pollock2 & 7 & 2 & Z & Y & 0.8036 & 0.0714 & 0.125 \\  & Pollock3 & 2 & 2 & N & N & 0.8571 & 0.0714 & 0.0714 \\  & Pollock4 & 2 & 2 & Z & N & **0.875** & 0.0357 & 0.0893 \\ \hline \multirow{9}{*}{B2} & Sphere1 & 8 & 1 & N & Y & 0.6637 & 0.1327 & 0.2035 \\  & Sphere2 & 2 & 1 & N & N & 0.7788 & 0.1858 & 0.0354 \\  & Sphere3 & 2 & 1 & Z & N & **0.8142** & 0.1593 & **0.0265** \\  & Cylinder1 & 8 & 2 & N & Y & 0.7611 & 0.1239 & 0.115 \\  & Cylinder2 & 9 & 2 & Z & Y & 0.7345 & 0.1416 & 0.1239 \\  & Cylinder3 & 2 & 2 & N & N & 0.7699 & 0.115 & 0.115 \\  & Cylinder4 & 2 & 2 & Z & N & 0.708 & **0.0796** & 0.2124 \\  & Pollock1 & 9 & 2 & N & Y & 0.7345 & 0.1504 & 0.115 \\  & Pollock2 & 9 & 2 & Y & Y & 0.7168 & 0.1681 & 0.115 \\  & Pollock3 & 2 & 2 & N & N & **0.8142** & 0.0973 & 0.0885 \\  & Pollock4 & 2 & 2 & Y & N & 0.8053 & 0.0885 & 0.1062 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Tree segmentation results of the benchmark plots using mean shift under various settings. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Shape** & **Crown** & **Match** & **Over Segment** & **Under Segment** & **RMSE\_xy** & **RMSE\_h** & **RMSE\_1** & **RMSE\_1** \\ \hline Cylinder1 & Center & **0.8222** & 0.0444 & **0.1333** & 1.27 & **0.837** & 1.1406 & 1.0332 \\ Cylinder1 & Top & 0.7778 & 0.0889 & **0.1333** & 1.3389 & 0.9926 & 1.1304 & 1.057 \\ Pollock1 & Center & **0.8222** & **0.0222** & 0.1556 & 1.1845 & 0.8863 & **0.9069** & **0.9093** \\ Pollock1 & Top & 0.7111 & 0.0889 & 0.2 & 1.2645 & 0.9304 & 0.9545 & 0.9169 \\ MK+WS & - & 0.6889 & 0.0667 & 0.2444 & 1.2276 & 0.9599 & 1.0969 & - \\ RegGrow & - & 0.7333 & 0.0444 & 0.2222 & 1.3047 & 0.9293 & 1.2549 & - \\ Vox+Rule & - & 0.7778 & 0.0667 & 0.1556 & **1.043** & 0.9511 & 3.1363 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Tree crown detection results of the Aberfoyle forest. The best Cylinder and Pollock kernel settings are tested to extract crown locations using either ellipse centers (Center) or top points (Top), compared with watershed (Mk+WS), region growing (RegGrow) and voxel-based ruling (Vox+Rule) methods. The best results are highlighted in **bold**. The accuracies of location, height and crown spreads of detected tree tops are shown by RMSEs compared to the ground truth respectively. the best had the same bandwidth, ratio, and adaptiveness settings. Weighting in Z has an inconsistent impact on the results, as both increases and decreases in match rates are observed compared to no weighting.