## 3 Proposed Method
### Classification of Network Construction  
Our proposed network is based on a fully convolutional segmentation network [15] with two branches, as shown in Figure 5. FCN transforms the fully connected layers in traditional CNNs into individual convolutional layers. Compared with the traditional method of image segmentation with CNNs, FCN has two significant advantages. Firstly, it can accept input images of arbitrary size without requiring all training and test images to have the same size. Secondly, it is more efficient because the problem of repeated storage and computation of convolution due to the use of pixel blocks is avoided. In order to extract features in a better way, speed up the convergence speed of the network, and reduce the problem of gradient descent, our basic unit includes the convolutional layer, the batch normalization (BN) layer, and the ReLU layer. The convolutional and ReLU layers are used to hierarchically extract high-level features and improve the non-linear representation power of the network. The middle BN layer has an important role in the training procedure. Firstly, it accelerates the convergence of the network. In CNNs, there is the phenomenon of internal covariate bias. With the BN layers, the distribution of the data in each layer converges and the training is easier to converge. Secondly, it prevents gradient explosion and gradient disappearance. Thirdly, it prevents overfitting. In the training of the network, BN makes all the samples in a minibatch correlated so that the network does not generate a definite result from a particular training sample. In Figure 5, we utilize 2D and 3D convolutions for extracting spectral, and spatial-spectral information, respectively. Then, the features extracted from the two branches are cascaded together and fed into the classification network to obtain the prediction results. The size of each layer of convolution kernel as well as its output size in Figure 5 are shown in Tables 1 and 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**Salinas Valley**} & \multicolumn{2}{c}{**Pavia University**} & \multicolumn{2}{c}{**Houston University**} \\ \cline{2-7}  & **Kernel Size** & **Output Size** & **Kernel Size** & **Output Size** & **Kernel Size** & **Output Size** \\ \hline input & - & \((204\times 204\times 36)\) & - & \((103\times 103\times 64)\) & - & \((144\times 144\times 36)\) \\ L1 & \((10\times 10\times 1)\) & \((39\times 39\times 36\times 64)\) & \((6\times 6\times 1)\) & \((33\times 33\times 64\times 32)\) & \((10\times 10\times 1)\) & \((27\times 27\times 36\times 64)\) \\ L2 & \((3\times 3\times 1)\) & \((20\times 20\times 36\times 64)\) & \((3\times 3\times 1)\) & \((17\times 17\times 64\times 64)\) & \((3\times 3\times 1)\) & \((14\times 14\times 36\times 64)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameter details of the SFCI branch. Figure 5: The architecture of the proposed SSDT-net. The first branch is the spectral full-channel interaction (SFCI) branch which is used to extract the proposed spectral correlation feature. The existing research shows that the spatial features can provide complementary information to the spectral features, and the joint extraction of spectral-spatial information can significantly improve classification accuracy. Therefore, we add the spatial feature extraction (SAS) branch to extract spectral-spatial features jointly. The specific network parameters are described in Section 4.2. Finally, we fuse these two kinds of features adaptively and feed them into the classifier. Before the training, we employ the SSDT strategy to transform raw data into C\(\times\)C\(\times\) (H \(\times\) W) as input1 (204 \(\times\) 204 \(\times\) 36 for Salinas Valley). Considering the size of the input data block, we set the size of the convolution kernel to \(3\times 3\) or \(5\times 5\), etc. Since the dimensional transformation is performed in the SFCI- branch, the data of the SAS branch needs to be converted before the fusion.