# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 4 Experiments and Results
### Results of Benchmark Data
The Pollock model crown parameter was empirically set to 2 for both plots. This means that the same parameter settings can be applied to both plots, which demonstrates the applicability of the parameter settings to a larger area out of its own plot. Figure 6 depicts the segmentation results and detected tree tops (blue points) alongside the ground truth (red points). The mean shift method with both Cylinder and Pollock kernels was able to detect tree tops at different layers. The Pollock kernel detected more tree tops than the Cylinder kernel, and had higher match rates for both plots, while maintaining low oversegmentation rates. The watershed method was able to detect some of the dominant and about half of the co-dominant trees, but not intermediate or suppressed trees. It also showed a tendency to under detect trees on the edge of the data. The final detected number of trees was even lower than the ground truth which is a subset of the trees. The crown locations and heights were compared with ground truth under the two localization strategies to demonstrate the influence on tree segmentation assessment. The final segmentation and validation results are presented in Table 7. The crown centered by fitted ellipse improved the crown matching rate for both kernels in both plots. The match rates of the watershed and region growing methods were rather low, which was due to the fact that the plots were multi-layered, and that the raster-based method is less capable of capturing the lower layers. The RMSEs in locations (RMSE_xy) and height (RMSE_h) of the mean shift method varied slightly, whether the crown was localized or not, and they are generally better than that of the two raster-based methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Plot** & **Shape** & **Crown** & **Match** & **Over Segment** & **Under Segment** & **RMSE\_xy** & **RMSE\_h** \\ \hline \multirow{4}{*}{B1} & Cylinder1 & Center & 0.8393 & 0.0536 & 0.1071 & 1.0824 & 0.8175 \\  & Cylinder1 & Top & 0.7679 & 0.0536 & 0.1786 & **0.9503** & **0.7907** \\  & Pollock4 & Center & **0.875** & **0.0357** & **0.0893** & 0.9639 & 0.952 \\  & Pollock4 & Top & 0.8036 & 0.0536 & 0.1429 & 0.9665 & 0.9338 \\  & Mk+WS & - & 0.5893 & 0.0357 & 0.375 & 0.9731 & 1.8755 \\  & RegGrow & - & 0.6607 & 0.0714 & 0.2679 & 1.2175 & 2.2072 \\ \hline \multirow{4}{*}{B2} & Sphere3 & Center & **0.8142** & 0.1593 & **0.0265** & **1.0114** & 0.7787 \\  & Sphere3 & Top & 0.7788 & 0.1593 & 0.0619 & 1.0429 & **0.7547** \\  & Pollock3 & Center & **0.8142** & 0.0973 & 0.0885 & 1.1278 & 1.0253 \\  & Pollock3 & Top & 0.7611 & 0.0973 & 0.1416 & 1.1125 & 0.9702 \\  & Mk+WS & - & 0.5398 & 0.0619 & 0.3982 & 1.8795 & 0.7606 \\  & RegGrow & - & 0.6018 & **0.0442** & 0.354 & 1.3739 & 1.5071 \\ \hline \end{tabular}
\end{table} 
Table 7: Tree top detection results for the benchmark plots. The best kernel settings are tested to extract tree crowns using either ellipse centers (Center) or top points (Top), compared with watershed (Mk+WS) and region growing (RegGrow) methods. The best results are highlighted in **bold**. The accuracies of locations and heights of detected tree tops are shown by root mean square errors compared to the ground truth (RMSE_xy, RMSE_h), respectively. More individual tree delineation methods were tested on the benchmark data as presented in [22]. The comparisons with other methods are shown in Table 8. The mean shift performed the best in plot B1, but was outperformed by the FGI method in plot B2. Figure 6: Segmentation and tree top detection results of the benchmark plots (first two rows: plot B1, second two rows: plot B2) from three methods (scale in meters): (**a**,**d**) Mean shift with Cylinder kernel; (**b**,**e**) Mean shift with Pollock kernel; (**c**,**f**) Marker-controlled watershed method. Red points denote true tree tops, and blue ones are detected tops.