## 3 Experimental Results and Analysis
### Experimental Settings  
Parameter settings are essential for the performance of models. For the fairness of the experiment, although the data resolution is different, the resolution of the ground truth is the same. The experiment was implemented with Pytorch in Python3. In M-SSL, the input is pretrained as the input from the pretext task. MVAE consists of h-VAE and m-VAE. Similarly, MAAE consists of h-AAE and m-AAE. Tables 2 and 3 show the encoder and decoder structure of AAE for HSI, and Tables 4 and 5 show the encoder and decoder structure of AAE for MSI. The difference between them is that the additional processing of dimensionality reduction is operated for HSI. The Conv3d and Smish layers [39] are connected to be used in MAAE or MVAE, due to the effectiveness of the Smish activation function. The latent code dimension is 128, the feature extracted by the pooling layer of the AAE output dimension of 1024 d. In the training part, the encoder, the decoder, the generator, and the discriminator are all optimized by two Adam optimizers. Whereas both the learning rate of the optimizer for the encoder and decoder were set at 0.001, and the weight decay of them was set to 0.0005, similarly, the learning rate of the generator module was set to 0.0001 and without weight decay, and the learning rate of the discriminator was set to 0.00005 without weight decay. The size of the batch is set as 128. In the experiment, we trained the MAAE model for 30 epochs and just took the parameters from the encode module. Figure 5: The architecture of the M-SSL for tree species classification. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**m-AAE Encoder Parameter Setting**} \\ \hline
**Layer** & **The Shape of Output** \\ \hline Input & \([-1,1,1,15,15]\) \\ Con3d & \([-1,8,9,13,13]\) \\ BatchNorm3d & \([-1,8,9,13,13]\) \\ Smish & \([-1,8,9,13,13]\) \\ Con3d & \([-1,16,5,11,11]\) \\ BatchNorm3d & \([-1,16,5,11,11]\) \\ Smish & \([-1,16,5,11,11]\) \\ Con2d & \([-1,16,5,11,11]\) \\ BatchNorm2d & \([-1,32,9,9]\) \\ Smish & \([-1,32,9,9]\) \\ Con2d & \([-1,32,9,9]\) \\ Con2d & \([-1,64,7,7]\) \\ BatchNorm2d & \([-1,64,7,7]\) \\ Smish & \([-1,64,7,7]\) \\ AdapAvgPool2d & \([-1,64,4,4]\) \\ Linear & \([-1,512]\) \\ Smish & \([-1,512]\) \\ Linear & \([-1,512]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The shape of layers in the m-AAE model for encoding. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**h-AAE Encoder Parameter Setting**} \\ \hline
**Layer** & **The Shape of Output** \\ \hline Input & \([-1,1,15,15,15]\) \\ Con3d & \([-1,8,13,13,13]\) \\ BatchNorm3d & \([-1,8,13,13,13]\) \\ Smish & \([-1,8,13,13,13]\) \\ Con3d & \([-1,16,11,11,11]\) \\ BatchNorm3d & \([-1,16,11,11,11]\) \\ Smish & \([-1,16,11,11,11]\) \\ Con2d & \([-1,32,9,9]\) \\ BatchNorm2d & \([-1,32,9,9]\) \\ Smish & \([-1,32,9,9]\) \\ Con2d & \([-1,64,7,7]\) \\ BatchNorm2d & \([-1,64,7,7]\) \\ Smish & \([-1,64,7,7]\) \\ AdapAvgPool2d & \([-1,64,4,4]\) \\ Linear & \([-1,512]\) \\ Smish & \([-1,512]\) \\ Linear & \([-1,128]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The shape of layers in the h-AAE model for encoding. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**h-AAE Encoder Parameter Setting**} \\ \hline
**Layer** & **The Shape of Output** \\ \hline Input & \([-1,1,15,15]\) \\ Con3d & \([-1,8,13,13,13]\) \\ BatchNorm3d & \([-1,8,13,13,13]\) \\ Smish & \([-1,8,13,13,13]\) \\ Con3d & \([-1,16,11,11,11]\) \\ BatchNorm3d & \([-1,16,11,11,11]\) \\ Smish & \([-1,16,11,11,11]\) \\ Con2d & \([-1,32,9,9]\) \\ Smish & \([-1,32,9,9]\) \\ Con2d & \([-1,32,9,9]\) \\ BatchNorm4d & \([-1,64,7,7]\) \\ Smish & \([-1,64,7,7]\) \\ AdapAvgPool2d & \([-1,64,4,4]\) \\ Linear & \([-1,512]\) \\ Smish & \([-1,512]\) \\ Linear & \([-1,512]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The shape of layers in the h-AAE model for decoding. Tables 6 and 7 show the encode and decode structures of MVAE for HSI (h-VAE), and Tables 8 and 9 show the encode and decode structure of the MVAE for MSI (m-VAE).