# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 3 Results
### Training DBMF  
During the training of the CNN model, the size of the convolution kernel has a great impact on classification accuracy [45]. If the convolution kernel is set to be larger, the detailed information will be lost, but the associated data will be retained. On the contrary, if the size is set to be smaller, the result is just the opposite. Therefore, the size of the convolution kernel directly affects the results of tree species classification. In this experiment, the most common sizes of convolution kernels are used with \(n\), \(n=1\), \(3\), \(5\), \(7\), \(9\), \(11\), and \(13\). The model was trained with a batch size of 100 samples during 1000 epochs, and the result of which is presented in Figure 4. From Figure 5, it can be seen that the size 7 had the highest accuracy rate. Size 9 converged rapidly in the early stage, which almost reached the peak in 800 cycles, but the accuracy rate did not rise from then on, and therefore the learning ability was limited. However, from the subfigure of the size 7 learning curve, the accuracy still improved in the stage of 800 epochs, the gradient of which did not completely disappear. Although size 1 and size 3 were stable, the accuracy rate was unable to satisfy the given request. The curves of size 11 and size 13 fluctuated considerably, which were not suitable for mass machine learning operations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **HSI Tunnel** & & **MSI Tunnel** & \\ \hline \multirow{2}{*}{**Layer**} & \multirow{2}{*}{**Kernel Size**} & **Output** & \multirow{2}{*}{**Layer**} & **Kernel Size** & **Output** \\  & & & **Shape** & & **Shape** \\ \hline Input & & (\(7\times 7\times 100\)) & Input & (\(7\times 7\times 100\)) \\ \hline Bidirectional & (\(1\times 1\times 7\)) & (\(7\times 7\times 47\), 24) & BN-Lfu-Conv & (\(3\times 3\times 1\)) & (\(7\times 7\times 1\), 24) \\ \hline Permute & & (\(7\times 7\times 47\), 24) & Concatenate & (\(7\times 7\times 1\), 24) \\ \hline Triple-Attention & & (\(7\times 7\times 47\), 24) & Sandglass Block & (\(7\times 7\times 1\), 24) \\ \hline BN-Lfu-Conv & (\(1\times 1\times 47\)) & (\(7\times 7\times 1\), 60) & BN-Lfu-Conv & (\(3\times 3\times 1\)) & (\(7\times 7\times 1\), 24) \\ \hline Pooling & (\(7\times 7\times 1\)) & (\(1\times 1\times 1\), 60) & Concatenate & (\(7\times 7\times 1\), 24) \\ \hline Concatenate & & (\(1\times 60\)) & Sandglass Block & (\(7\times 7\times 1\), 24) \\ \hline  & & \multicolumn{2}{c}{Concatenate} & (\(1\times 60\)) \\ \hline Layers (Fusion) & & \multicolumn{2}{c}{Output Shape (Fusion)} \\ \hline JOINT & & (\(1\times 60\)) & \\ \hline BN-MISH-Conv & & (\(7\times 7\times 1\), 60) & \\ \hline BN-Dropout-Pooling-Lfu & & (\(1\times 60\)) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DBMF architecture. The size of the parameter file of the DBMF with all layers was 303 megabytes. The stochastic gradient descent method was also used for training. Figure 5 shows the training time consumed by the tested models, where DBMF took approximately 100 min, which was considerably faster (30 min) than CD-CNN, whose learning speed was the fastest in other compared methods, while SVM took the longest time. It is shown that DBMF was the fastest learning model compared with the others, and the DL capability was better than traditional machine learning in tree species classification. Figure 5(d) shows the changes in accuracy and loss rate in the training process of the model. Increasing the number of iterations changed the whole network model so the complete training process can be observed in time and avoid overfitting. As the number of  
Figure 4: The accuracy curve of DBMF with different kernel sizes. Note: The subfigures are ordered by sizes 1, 3, 5, 7, 9, 11, and 13. Figure 5: Training time. iterations slowly increased, the accuracy also gradually increased. When the epoch number approached 400, the accuracy tended to be stable. Similarly, as the number of iterations increased, the loss value gradually decreased.