## 4 Results and Discussion
### Land/Water Discrimination from Seeded Region Growing  
The point feature _\"NOR\"_ was used to find the possible seed points by detecting the points that had single returns in C1 or C2 (_points_single_) and the first of the double returns in C3 (_points_first_of_double_), as shown in Figure 8a-c, respectively. The _NOR_ was used rather than other features (e.g., _HV_ or _HSD_) because it achieved a higher correctness of 95.2% (C1C3) and 96.5% (C2C3) for the water body discrimination. For each point in _points_single_, the nearest point from and within the circle footprint of _points_first_of_double_ was found. The searching radius was set to 0.15 m, which was calculated by multiplying the flying height (430 m) by half of the beam divergence of C3 (0.35 mrad). The selected points might represent other land objects, such as vegetation, which could have the same characteristics. The extracted _points_single_ and _points_first_of_double_ are shown in Figure 8d,e. Therefore, the _HV_ was checked within a 10 m searching radius to be less 0.5 m in order to preserve \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Point Feature**} & \multirow{2}{*}{**Channels**} & \multicolumn{2}{c}{**Completeness (\%)**} & \multicolumn{2}{c}{**Correctness (\%)**} & \multirow{2}{*}{**Overall Accuracy (\%)**} \\ \cline{3-3} \cline{5-7}  & & **Land** & & **Water** & & **Land** & **Water** \\ \hline \multirow{2}{*}{_PD_} & C1C3 & 27.4 & **97.1** & **94.1** & 44.4 & 53.4 \\  & C2C3 & **44.1** & 95.3 & 94.0 & **50.4** & **63.2** \\ \hline \multirow{2}{*}{_NOR_} & C1C3 & 97.7 & 75.8 & 87.1 & 95.2 & 89.5 \\  & C2C3 & **98.3** & **78.2** & **88.3** & **96.5** & **90.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy measures of labeled points based on _PD_ and _NOR_. The best results are highlighted in bold. Figure 7: Labeled LiDAR points based on _PD_ and _NOR_. horizontal surfaces (i.e., water surface). Figure 8f,g shows the output that represents the water seed points. These points were used as an input for region growing, where the \(HV\) was checked point by point to be less than 0.5 m so that all water surface points could be labeled accurately. Figure 8h,i shows the labeled LiDAR points using the combination of C3 with either C1 or C2, while Table 8 provides the accuracy measures for the two cases. Using either C1 or C2 together with C3 achieved a high overall accuracy of more than 99% when using the automatic seeded region growing algorithm. The completeness and correctness of the water class is also high at more than 94%. The completeness of the water class was affected by the misclassification of a small pond at the edge of the dataset. The completeness of the combination of C1 with C3 (94%) was smaller than that of C2 with C3 (95.7%) because the misclassified part at the water edges in the first combination was higher than the misclassified part in the second combination. Figure 8: LiDAR points based on seeded region growing algorithm. (**a–c**) Single Returns of C1, C2 and C3; (**d,e**) Possible Seed Points using C1 with C3 and C2 with C3; (**f,g**) Water Seed Points using C1 with C3 and C2 with C3; (**h,i**) Seeded Region Growing using C1 with C3 and C2 with C3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Channels**} & \multicolumn{2}{c}{**Completeness (\%)**} & \multicolumn{2}{c}{**Correctness (\%)**} & \multirow{2}{*}{**Overall Accuracy (\%)**} \\ \cline{2-2} \cline{4-5}  & **Land** & **Water** & **Land** & **Water** \\ \hline C1C3 & **99.9** & 94.0 & 99.0 & 99.2 & 99.0 \\ C2C3 & **99.9** & **95.7** & **99.1** & **99.4** & **99.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy measures of labeled points based on seeded region growing. The best results are highlighted in bold. This high accuracy rate shows the benefit of using multi-channel in such applications. However, this approach requires recorded returns at the green wavelength from the surface and benthic layer of water bodies in order to fully automate the discrimination process.