## 3 Experiment and Parameter Selection
### The Number of Target Domain Samples  
The sample size of the target domain data set has an important impact on transfer learning. The size of the sample directly affects the generalization ability of the model. The simulation results of different scale samples give the following results:  
Table 2 shows the classification results of the two transfer learning methods as a function of the sample size. The M in DBN-M represents the number of transfer layers. M = 0 indicates that the results of the transfer learning method are not used. Figure 13 shows the results for each sample size of 30, 60, 90, and 120. From the trend of the sample size in the figure, the accuracy of the model varies with the sample size. When the sample size is 30 and 60 per class, the transfer learning has a good effect, and the effect of improving the accuracy is obvious. With the increase in the sample size, when the number of samples reaches 90 and 120, the effect of transfer learning on the model's results is not obvious.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample Size**} & \multicolumn{4}{c}{**DBN Transfer Model**} \\ \cline{2-5}  & **DBN-0** & **DBN-1** & **DBN-2** \\ \hline
30 & 0.759 & **0.832** & 0.824 \\
60 & 0.821 & 0.862 & **0.872** \\
90 & 0.886 & **0.895** & 0.893 \\
120 & 0.81 & 0.9012 & **0.9283** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The classification results of different target regions’ transfer learning based on the sample size.  
Figure 12: Accuracy changes with the number of transfer layers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**The Number of Layers from the Source Domain Data to the Target Domain Data**} \\ \cline{2-5}  & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline TSF-DBN-LR & 0.891 & 0.9012 & 0.9283 & 0.9108 & 0.8831 & 0.8666 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Deep neural network classification of different transfer results.  
Figure 13: Change in accuracy versus target domain sample.  
As seen from Figure 15, when the data size is increased to 30 per class, by comparing the classification results of different algorithms, it can be seen from the results of the above figure that deep learning combined with the SVM algorithm can achieve better classification results by using transfer learning and the deep learning algorithm DBN. The deep transfer learning model can effectively improve the classification accuracy compared with the traditional feature extraction PCA algorithm. Compared with the data without the dimensionality reduction, the dimensionality reduction and feature extraction process can effectively reduce the complexity of the data and improve the data divisibility so that the classification accuracy is greatly improved. Therefore, it can be concluded that feature extraction can obviously improve the data divisibility. Furthermore, different classification algorithms have a certain impact on the classification accuracy. In contrast, the SVM is the best SVM algorithm in comparison with the KNN and NBM algorithms.  
The SVM classification results, the PCA-SVM classification results, the DBN-SVM classification results, and the DBN-SVM spatial classification results are shown in Figure 16. Through the comparison experiment, we can see that the proposed algorithm based on the transfer learning feature can achieve higher accuracy, and the accuracy can be improved by combining the spatial information, as informed in Section 3.1.