## 3 Results
### Classification Results of Conv1D Model and Random Forest Model
In addition, according to the coordinate position of validation sample data, we found that for less abundant tree species (Chinese pine, Dahurian larch, and aspen), the Conv1D-based classification results are superior to the RF classification results, but misclassification still exists. In addition, for some broadleaf species with similar spectral characteristics (white birch, Manchurian ash, and Manchurian walnut), Conv1D classifier tended to correctly map these areas. In general, the RF classifier tends to omit species and identify more pixels as other species, and the Conv1D-based classifier is more likely to seek for \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference Classes**} & \multicolumn{8}{c}{**Classified**} & \multicolumn{2}{c}{**Producer’s**} \\ \cline{2-10}  & **AL** & **CP** & **DL** & **AP** & **WB** & **MW** & **MA** & **Total** & **Accuracy (\%)** \\ \hline
**Amur linden (AL)** & 1253 & 0 & 16 & 61 & 140 & 86 & 67 & 1623 & 77.18\% \\
**Chinese pine (CP)** & 0 & 177 & 42 & 3 & 21 & 14 & 55 & 311 & 56.78\% \\
**Dahurian larch (DL)** & 0 & 15 & 216 & 1 & 10 & 3 & 33 & 276 & 78.02\% \\
**Aspen (AP)** & 21 & 0 & 12 & 222 & 19 & 6 & 7 & 288 & 77.28\% \\
**White birch (WB)** & 27 & 0 & 2 & 10 & **991** & 55 & 29 & 1114 & 88.97\% \\
**Manchurian walnut (MW)** & 136 & 2 & 1 & 78 & 250 & 1399 & 83 & 1949 & 71.77\% \\
**Manchurian ash (MA)** & 11 & 30 & 21 & 8 & 18 & 101 & 1952 & 2141 & 91.15\% \\
**Total** & 1448 & 224 & 310 & 383 & 1448 & 1663 & 2225 & 7702 & \\
**User’s Accuracy (\%)** & 86.52\% & 78.72\% & 69.44\% & 57.99\% & 68.43\% & 84.08\% & 87.73\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of the test set by the RF classifier. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference Classes**} & \multicolumn{8}{c}{**Classified**} & \multicolumn{2}{c}{**Producer’s**} \\ \cline{2-10}  & **AL** & **CP** & **DL** & **AP** & **WB** & **MW** & **MA** & **Total** & **Accuracy (\%)** \\ \hline
**Amur linden (AL)** & 1148 & 0 & 6 & 18 & 261 & 128 & 61 & 1623 & 70.75\% \\
**Chinese pine (CP)** & 0 & 185 & 24 & 2 & 9 & 2 & 90 & 311 & 59.58\% \\
**Dahurian larch (DL)** & 1 & 9 & 222 & 0 & 13 & 2 & 29 & 276 & 80.52\% \\
**Aspen (AP)** & 18 & 0 & 0 & 251 & 15 & 4 & 0 & 288 & 87.15\% \\
**White birch (WB)** & 26 & 11 & 7 & 4 & 987 & 38 & 41 & 1114 & 88.65\% \\
**Manchurian walnut (MW)** & 40 & 4 & 6 & 12 & 28 & 1736 & 123 & 1949 & 89.10\% \\
**Manchurian ash (MA)** & 15 & 22 & 29 & 1 & 20 & 36 & 2019 & 2141 & 94.30\% \\
**Total** & 1247 & 230 & 295 & 287 & 1333 & 1945 & 2364 & 7702 & \\
**User’s Accuracy (\%)** & 92.07\% & 80.50\% & 75.45\% & 87.23\% & 74.06\% & 89.25\% & 85.43\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of the test set by the Conv1D-based classifier. further separation of the seven tree species. Thus, this further confirms the higher capability of the Conv1D-based classifier to discriminate tree species with respect to the RF classifier. Figure 6: (**a**) Classification map by Conv1D-based classifier; (**b**) classification map by random forest (RF) classifier.