# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 4 Results
### Comparison of Single-Wavelength and Multi-Wavelength LiDAR  
To compare the accuracy of land cover classification between single- and multi-wavelength LiDAR systems, we designed four combinations (Table 4) including the features intensity, nDSM, and curvature. The first three combinations were single-wavelength LiDAR features from each channel, respectively. The last combination merged all features from three channels, totaling nine, including three intensities, three nDSMs, and three curvatures. The segmentation of single-wavelength LiDAR only considered its own intensity, whereas the segmentation of multi-wavelength LiDAR considered three intensities simultaneously. The classification employed the same training areas but different features. As previously described, the test area was classified into five land cover classes (buildings, trees, grass, roads, and bare soil), and the results from the four combinations were color-coded to indicate each land cover type (Figure 5). The quantitative results (i.e., completeness and correctness; Table 5 and Figure 6) show that, as expected, the accuracy of the green channel was lower than the other two channels in infrared because the green channel is designed to represent water surface in most situation. The completeness of roads for 532 nm (Case 1-1) was only 59%, lower than other cases, meaning that the infrared channel performs better than the green channel for asphalts road. Therefore, the intensity of infrared is widely used to discriminate between asphalt and non-asphalt roads [36]. The completeness values of bare soil for 532 nm (Case 1-1) and 1064 nm (Case 1-2) were less than 52% because soil, road, and grass have similar height and curvature in the single-wavelength system. The completeness of 1550 nm (Case 1-3) is slightly better than the other two channels because it is near-to-mid infrared. [PERSON] et al. (2012) [8] also found that results from 1550 nm were better than those from 1064 nm. The integration of all features (Case 1-4) provided better discrimination among soil, road, and grass than single-wavelength features. \begin{table}
\begin{tabular}{c c} \hline \hline
**Combinations** & **Features** \\ \hline Case 1-1 & \(nDSM_{\lambda 532}\), \(Curvature_{\lambda 532}\), \(Intensity_{\lambda 532}\) \\ Case 1-2 & \(nDSM_{\lambda 1064}\), \(Curvature_{\lambda 1064}\), \(Intensity_{\lambda 1064}\) \\ Case 1-3 & \(nDSM_{\lambda 1550}\), \(Curvature_{\lambda 1550}\), \(Intensity_{\lambda 1550}\) \\ Case 1-4 & All features of Case 1-1, Case 1-2 and Case 1-3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The combinations for single- and multi-wavelength LiDAR classification. Because trees and buildings are significantly higher than other land covers, they show limited improvement when the three wavelengths were integrated. Overall, the completeness improvement from single- to multi-wavelength LiDAR ranged from 1.7 to 42.3 percentage points. In correctness analysis, the behavior of soil, road, and grass was similar to results from completeness. The multi-wavelength features (Case 1-4) also improved the correctness of these three classes from 1.4 to 35.8 percentage points. The soil and grass had a higher improvement rate when the multi-wavelength feature was adopted. The decrease in completeness is less than 2.5 percentage points between cases 1-1 and 1-4 for grass. For correctness, the decrease in accuracy is less than 0.2 percentage points between cases 1-2 and 1-4 for tree. The classification results lead to overall accuracy (OA) from individual channels of 82%, 90%, and 92% from wavelengths 532, 1064, and 1550 nm, respectively. The OA and kappa coefficient achieved 96% and 95%, respectively, using extracted features from the multi-wavelength LiDAR system The OA of multi-wavelength LiDAR increased 4-14 percentage points compared to single-wavelength LiDAR (Figure 7). The single-wavelength LiDAR extracts limited information and caused misclassifications when the classes had similar reflectivity, such as that for roads and grass in the green channel.