## 3 Results
### Classification Results of Conv1D Model and Random Forest Model  
We computed confusion matrices, overall accuracy (OA), and kappa coefficients (kappa) of the validation set to evaluate the performance of two classifiers. Tables 3 and 4 show confusion matrices yielded by RF and Conv1D-based classifiers, respectively. Classification errors are represented by pixel numbers off the diagonal in the confusion matrix. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Types** & **Indices** & **Description** \\ \hline MNF & MNF1 & The first principal component of minimum noise fraction \\ \hline  & MNF2 & The second principal component of minimum noise fraction \\ \hline  & MNF3 & The third principal component of minimum noise fraction \\ \hline Band reflectance & B1, B2, B3 & Blue, B1:466 nm, B2:480 nm, B3:500 nm \\ \hline  & B4, B5, B6, B7, B8 & Green, B4:520 nm, B5:536 nm, B6:550 nm, B7:566 nm, \\  & B8:580 nm \\ \hline  & B9, B10, B11, B12, B13, B14, & Red, B9:596 nm, B10:610 nm, B11:626 nm, B12:640 nm, \\  & B15, B16 & Red, B13:656 nm, B14:670 nm, B15:689 nm, B16:700 nm \\ \hline  & B17, B18, B19, B20, B21, B22 & Red edge, B17:716 nm, B18:730 nm, B19:746 nm, B20:760 \\  & & nm, Red edge, B21:776 nm, B22:790 \\ \hline  & B23, B24, B25, B26, B27, & Near infrared, B23:806 nm, B24:820 nm, B25:836 nm, \\  & B26, B26, B850 nm, B27:866 nm, B28:880 nm, B29:896 nm, \\  & Near infrared, B30:910 nm, B31:926 nm, B32:940 nm \\ \hline Vegetation indices & RVI & Ratio vegetation index, B28-B14 \\ \hline  & NDVI & Normalized difference vegetation index, \\  & & (B23 - B14)/(B23 + B14) \\ \hline  & EVI & Enhanced vegetation index, \\  & & \(2.5\times(\text{B23}-\text{B14})/(\text{B23}+6.0\times\text{B14}-7.5\times \text{B2}+1)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Remote sensing indices from OHS-1 hyperspectral data for features extraction. Figure 5: Feature contribution rate and its impact on accuracy. According to the confusion matrices, the overall accuracies of the Conv1D-based and RF classifiers were 85.04% and 80.61%, and the kappa coefficients were 0.81 and 0.75, respectively. Compared to the results by the RF classifier, the Conv1D-based classifier has better performance and balance between producer's and user's accuracies. For the Conv1D-based classifier, it is worth noting that aspen, white birch, Manchurian walnut, and Manchurian ash reached the highest accuracies. The producer's accuracies for these four classes were 87.15%, 88.65%, 89.10%, and 94.30%, respectively. However, only the white birch and Manchurian ash achieved higher classification accuracy (88.97% and 91.15%) in the RF classification results. The species that obtained the lowest producer's accuracies was Chinese pine in both Conv1D-based and RF classifier (59.58% and 56.78%). Regarding the user's accuracy, except for the accuracy of Manchurian ash being slightly lower than RF results; other tree species had better classification accuracies in the Conv1D-based classifier. It is particularly noticeable that there was significant variation when considering the accuracies of individual species. For example, the user accuracy of aspen based on the Conv1D classifier was 87.23%, while that of the RF classifier was 57.99%. Figure 6 shows the classification maps obtained with the Conv1D-based classifier and the RF classifier. Assessed against field observations, a comparative analysis of the two classification maps shows that, on the whole, the two classification methods can roughly distinguish different species types. However, for some species with a small proportion, the classification results are quite different. In general, the RF map tended to be more speckled than the Conv1D map. The RF map had more obvious problems with classifying white birch and Amur linden in the northwest corner of the map. The insets show a classical example of the classifications in the broadleaf zone. Compared with the Conv1D map, the RF map has obvious over-mapping for broad-leaved species.