## 3 Experimental Results and Analysis
### Experimental Settings
Similar to the above module, the dimension of the latent code is set to 128, and the features extracted by the pooling layer of VAE output the size of 1024 d. In the training part, the optimizers are employed in the same as the above module. The learning rate of the encoder and decoder were all set to 0.002, and their weight decay was set to 0.0003. The HSI and MSI latent codes were calculated by the VAE encoder, respectively. The batch size of them was set as 64. The MVAE module was trained for 50 epochs to save the parameters of encoders with HSI and MSI, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**h-VAE Decoder Parameter Setting**} \\ \hline
**Layer** & **The Shape of Output** \\ \hline Input & \([-1,128]\) \\ Linear & \([-1,512]\) \\ Sinish & \([-1,512]\) \\ Linear & \([-1,21104]\) \\ Smish & \([-1,21104]\) \\ ConTrans2d & \([-1,64,9,9]\) \\ BatchNorm2d & \([-1,64,9,9]\) \\ Smish & \([-1,64,9,9]\) \\ ConTrans2d & \([-1,32,11,11]\) \\ BatchNorm2d & \([-1,32,11,11]\) \\ Smith & \([-1,32,11,11]\) \\ ConTrans3d & \([-1,16,13,13,13]\) \\ BatchNorm3d & \([-1,16,13,13,13]\) \\ Smish & \([-1,16,13,13,13]\) \\ ConTrans3d & \([-1,8,15,15,15]\) \\ BatchNorm3d & \([-1,1,15,15,15]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The shape of layers in the h-VAE model for decoding. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**m-VAE Encoder Parameter Setting**} \\ \hline
**Layer** & **The Shape of Output** \\ \hline Input & \([-1,1,12,15,15]\) \\ 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 & \([-1,32,9,9]\) \\ 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 & \([-1,1,512]\) \\ Linear & \([-1,1,128]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The shape of layers in the m-VAE model for decoding. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**m-VAE Decoder Parameter Setting**} \\ \hline
**Layer** & **The Shape of Output** \\ \hline Input & \([-1,128]\) \\ Linear & \([-1,512]\) \\ Smish & \([-1,512]\) \\ Linear & \([-1,12704]\) \\ Smish & \([-1,12704]\) \\ ConTrans2d & \([-1,64,9,9]\) \\ BatchNorm2d & \([-1,64,9,9]\) \\ Smish & \([-1,64,9,9]\) \\ ConTrans2d & \([-1,64,9,9]\) \\ Smish & \([-1,64,9,9]\) \\ ConTrans2d & \([-1,32,11,11]\) \\ BatchNorm2d & \([-1,32,11,11]\) \\ Smish & \([-1,32,11,11]\) \\ ConTrans3d & \([-1,32,11,11]\) \\ BatchNorm3d & \([-1,16,11,13,13]\) \\ Smish & \([-1,16,11,13,13]\) \\ ConTrans3d & \([-1,16,11,13,13]\) \\ BatchNorm3d & \([-1,8,12,15,15]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The shape of layers in the m-VAE model for encoding. Most of the encoders in the supervised network have the same structure, as shown in Table 10. The input of HSI and MSI is discriminated to fusion by DCAM module, both dimensions are 1024, and the dimensions of the contrast features and the projection features are both 64 d. In M-SSL, the Adam optimizer is applied to train mostly from supervised networks. The learning rate = 0.003, and weight decay = 0.001. Negative sample number \(r\) = 640, batch number 128, \(\tau\) = 0.01 Equation (8), and cluster number K = [2000, 2500, 3000]. According to Equation (8), M-SSL was trained for 50 epochs. M-SSL was trained for 200 epochs based on Equation (10). When training epochs between 100 and 130, the learning rate is multiplied by 0.1.