## 3 Experimental Results and Analysis
### Comparative Experiment  
In the study, the results of tree species classification were evaluated by overall accuracy, average accuracy (OA, AA), and Kappa. Several comparative models were studied, which include linear discriminant analysis (LDA) [38], deep convolutional neural network (1D-CNN) [39], and supervised deep feature extraction (S-CNN) [40]. In addition, three unsupervised models were compared with M-SSL that are VAE, AAE, and the contrastive unsupervised feature learning method (CUFL) [30]. The results of tree species classification were obtained from the average records of three experiments for all models. To verify the ability of M-SSL for beneficial feature learning, the classification of tree species was performed as a downstream task. In addition, the Softmax is employed to highlight the effectiveness of extracted features, which are mainly derived from a supervised learning network. Tables 11 and 12 present classification results of three tree species datasets with the above seven models. The supervised and unsupervised models were tested, and their results are listed next. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**M-SSL Parameter Setting**} \\ \hline
**Layer** & **The Shape of Output** \\ \hline HSI Input & [\(-\)1, 1024] \\ MSI Input & [\(-\)1, 1024] \\ ConTrans2d & [\(-\)1, 64, 6, 6] \\ BatchNorm2d & [\(-\)1, 64, 6, 6] \\ Smish & [\(-\)1, 64, 6, 6] \\ ConTrans2d & [\(-\)1, 64, 8, 8] \\ BatchNorm2d & [\(-\)1, 64, 8, 8] \\ Smish & [\(-\)1, 64, 8, 8] \\ Con2d & [\(-\)1, 128, 6, 6] \\ BatchNorm2d & [\(-\)1, 128, 6, 6] \\ Smish & [\(-\)1, 128, 6, 6] \\ Con2d & [\(-\)1, 64, 4, 4] \\ BatchNorm2d & [\(-\)1, 64, 4, 4] \\ Smish & [\(-\)1, 64, 4, 4] \\ Con2d & [\(-\)1, 32, 2, 2] \\ Smish & [\(-\)1, 32, 2, 2] \\ Linear & [\(-\)1, 128] \\ Smish & [\(-\)1, 128] \\ Linear & [\(-\)1, 128] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The shape of layers in the M-SSL model. Table 11 and Figure 6 show the classification results of the dataset (1). According to them, M-SSL achieved the best performance that the OA of the dataset (1) classification is close to 80%. Since the tree species in the dataset (3) were slightly complex, it was difficult to classify, but M-SSL also reached 76%, and the classification effect was higher than in other models. Table 12 and Figure 7 show the classification results of the dataset (2). Similar to the dataset (1), M-SSL performed best in larch, and the classification results of the other five tree species also outperformed other models. However, according to the results of the dataset (1) in Table 13 and Figure 8, the average accuracy of M-SSL in this article is about 74%, which is better than that of AAE. Considering the overall performance of tree species classification, experimental results demonstrate the feasibility of M-SSL for multisource datasets. M-SSL is worthy of further study because it specializes in multisource datasets. Although the model works without label information, it is slightly weaker than supervised learning methods. In addition, compared to the best classification result of 68% by LDA, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Supervised Model**} & \multicolumn{3}{c}{**Unsupervised Model**} \\ \cline{2-9}  & **LDA** & **1D-CNN** & **S-CNN** & **CUFL** & **AAE** & **VAE** & **M-SSL** \\ \hline Birch & 71.70 & 72.20 & 76.32 & 79.44 & 78.23 & 75.32 & 78.18 \\ Larch & 72.89 & 80.37 & 80.92 & 73.69 & 79.38 & 80.31 & 80.82 \\ Spruce & 61.50 & 63.82 & 75.95 & 76.68 & 75.94 & 72.27 & 78.38 \\ Mongolia & 56.4 & 69.15 & 77.68 & 77.45 & 78.00 & 78.87 & 79.20 \\ Willow & 56.70 & 62.47 & 57.88 & 73.17 & 74.23 & 72.98 & 73.96 \\ Polar & 61.02 & 54.45 & 73.62 & 72.83 & 73.77 & 72.77 & 73.25 \\ OA (\%) & 68.65 & 69.61 & 76.25 & 76.76 & 77.41 & 75.83 & 80.60 \\ AA (\%) & 66.36 & 71.98 & 74.49 & 76.82 & 78.51 & 75.75 & 79.87 \\ Kappa (\%) & 65.71 & 68.17 & 66.81 & 75.30 & 76.42 & 74.31 & 79.17 \\ \hline \hline \end{tabular}
\end{table}