# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 4 Experiments and Results  
### Results of Simulated Data  
Extensive experiments were carried out to test the different configurations and parameters of the mean shift approach. As there are many possible combinations of parameter settings, the ones generating better results are reported. The segmentation results of the simulated data are presented in Table 2. The final F\({}_{1}\)-scores of sphere kernels are generally lower than the other two kernel shapes, Cylinder and Pollock. The best results from these two kernels are both under the configuration that b/a = 2.5, weighted in height, kernel is not adaptive, bandwidth a = 4. In general, the Pollock model kernels produced better results in terms of both match (recall) and precision, leading to a higher F\({}_{1}\)-score. According to the tests, the best performing Pollock model crown parameter was 1.5, regardless the settings of other parameters, which meant that this parameter needed only to be tested once. The kernels that performed the best, Cylinder3 and Pollock4, were taken to extract tree crown parameters, under the conditions of whether or not the crown is localized by an ellipse. In addition, crown parameters extracted from the segments generated by other compared methods were presented for comparison. The results are illustrated in Figure 4, in which the true tree tops are marked as red points, and the detected tree tops are in blue. It can be seen that for the majority of the trees, detected tree tops can be found nearby, using both Cylinder kernel (Figure 4a) and Pollock kernel (Figure 4b). However, there are few trees where no nearby tree tops are detected. The main reason is that the simulated trees are randomly located, in which case the gaps between trees can be much smaller than in real tree plots. Tree crowns are intertwined, hence they are difficult to separate. The watershed method yielded much worse results (Figure 4c) as trees located in close proximity were clumped together. Higher match rates (recalls) can be obtained when the kernels and searching radius are set to be smaller for both methods, but this will result in higher numbers of false detections, and thus reduce the Precision and final F\({}_{1}\)-score. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Shape** & **Band a** & **Ratio b/a** & **Weight** & **Adapt** & **Match** & **Over Segment** & **Under Segment** & **Precision** & **F\({}_{1}\)** \\ \hline Sphere1 & 3 & 1 & N & Y & 0.74 & 0 & 0.26 & 0.1063 & 0.1859 \\ Sphere2 & 5 & 1 & N & N & 0.76 & 0 & 0.24 & 0.3486 & **0.4780** \\ Sphere3 & 7 & 1 & Z & N & 0.4 & 0 & 0.6 & 0.5556 & 0.4651 \\ Cylinder1 & 4 & 2 & Z & N & 0.6 & 0 & 0.4 & 0.6667 & 0.6316 \\ Cylinder2 & 2 & 2 & N & N & 0.5 & 0 & 0.5 & 0.5682 & 0.5319 \\ Cylinder3 & 4 & 2.5 & Z & N & 0.6 & 0 & 0.4 & 0.6977 & **0.6452** \\ Cylinder4 & 4 & 1.5 & Z & N & 0.56 & 0 & 0.44 & 0.6087 & 0.5833 \\ Pollock1 & 4 & 2 & N & Y & 0.6 & 0 & 0.4 & 0.2804 & 0.3822 \\ Pollock2 & 4 & 2 & N & N & 0.56 & 0 & 0.44 & 0.6364 & 0.5957 \\ Pollock3 & 4 & 2 & Z & N & 0.62 & 0 & 0.38 & 0.7381 & 0.6739 \\ Pollock4 & 4 & 2.5 & Z & N & 0.66 & 0 & 0.34 & 0.7857 & **0.7174** \\ Pollock5 & 4 & 1.5 & Y & N & 0.62 & 0 & 0.38 & 0.775 & 0.6889 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tree segmentation results of the simulated data using mean shift under various settings. Three kernel shapes are tested: Sphere, Cylinder, and Pollock model, each of which is set to achieve the best result by changing the horizontal bandwidth a, vertical bandwidth ratio b/a, weight in XY, Z or None, adaptiveness (Y or N). Higher results of match, oversegmentation, undersegmentation, precision and F\({}_{1}\)-score, are listed. The highest F\({}_{1}\)-score for each kernel is highlighted in **bold** font. The tree crown parameter extraction results are presented in Table 3. The highest match rate and precision of mean shift are achieved when locating the crown by the top point (not the ellipse center from crown fitting) using the Pollok kernel.