## 4 Experiment
### Comparing with One-CNN  
To verify the effectiveness of the Dual-CNN model, we compare the proposed method with the 6 Ch-CNN and PauliRGB-CNN. We apply a Softmax classifier after their own respective last layers. In this way, we can train and test the networks with the same parameters. The training process of our method is performed iteratively 100 times on NVIDIA's GeForce GTX 1070 with 8 GB of GPU memory, and 1000 training samples are used in every epoch. Figures 5a, 6a and 7a show \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Color**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} \\ \cline{3-6}  & & & **6 Ch** & **PauliRGB** & **6 Ch** & **PauliRGB** \\ \hline
1 & Stembeans & & 5082 & 5082 & 1693 & 1693 \\
2 & Beets & & 6039 & 6039 & 2012 & 2012 \\
3 & Barley & & 5106 & 5106 & 1701 & 1701 \\
4 & Peas & & 5530 & 5530 & 1843 & 1843 \\
5 & Potatoes & & 9180 & 9180 & 3060 & 3060 \\
6 & Wheat2 & & 7343 & 7343 & 2447 & 2447 \\
7 & Forest & & 10,093 & 10,093 & 3364 & 3364 \\
8 & Bare soil & & 3299 & 3299 & 4099 & 4099 \\
9 & Wheat3 & & 12,663 & 12,663 & 4221 & 4221 \\
10 & Lucerne & & 6872 & 6872 & 2290 & 2290 \\
11 & grasses & & 4200 & 4200 & 1399 & 1399 \\
12 & Water & & 14,739 & 14,739 & 4913 & 4913 \\
13 & Wheat & & 12,361 & 12,361 & 4120 & 4120 \\
14 & Rapeseed & & 9013 & 9013 & 2838 & 2838 \\ Total & – & – & 111,520 & 111,520 & 37,000 & 37,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The detailed information of the training set and testing set on 14 types of land cover classes on Flevoland PolSAR data. Figure 4: (**a**) Pauli RGB image of Flevoland PolSAR data; (**b**) Ground-truth image of Flevoland. As shown in Figure 5, the loss curve of the 6 Ch-CNN is not very stable, and its accuracy curve has a large fluctuation as shown in Figure 5b. It is due to the fact that the 6 Ch-CNN cannot obtain the general features of the same class in complicated polarimetric data. Moreover, compared with the 6 Ch-CNN, although the loss curve and accuracy curve are stable in PauliRGB-CNN, the 3-channel data of the Pauli RGB image are scarce. This fact causes the accuracy rate of the PauliRGB-CNN to be below 95%. However, in Figure 7b, it can be found that the training accuracy of the Dual-CNN reaches 100%. The testing accuracy is becoming coincident, keeping at 98%. The training loss and the testing loss are stable except for the 45 th, 70 th and 75 th epoch. It proves that the polarimetric features and spatial features are well combined. In Figure 7a, the loss value of the Dual-CNN model decreases, beginning with a minor value 2.25, in which case the training accuracy is 50%, cutting down the training time and the number of epochs. That proved the validity of Equation (13). We draw the conclusion in terms of the accuracy that although there are some anomalies in the training set, the Dual-CNN model is not impacted. To clarify the result, we list the accuracy rate of the three different methods in Table 2. For the classification of 14 types of land cover classes, the lowest accuracy rate of the Dual-CNN model is still above 95%. Especially, the accuracies of Wheat2 (Label: 6) and Bare soil (Label: 8) reach 100%. However, the average accuracies of the 6 Ch-CNN and PauliRGB-CNN are 5.71% and 4.45% lower than the Dual-CNN model. For the convenience of comparison, the classified results are labelled using the same color as the ground-truth. The results of the ground-truth, Dual-CNN, 6 Ch-CNN and PauliRGB-CNN are shown in Figure 8a-d, respectively. Analyzing Figure 8c,d, we find that the 6 Ch-CNN and PauliRGB-CNN have obvious faults. The 6 Ch-CNN tends to have more scatter errors while PauliRGB-CNN tends to have block errors. However, the Dual-CNN only has a few scatter errors and almost no block errors, which is better than the single branch way. It also demonstrates that the combination of the 6 Ch-CNN and PauliRGB-CNN with a fully connected layer is effective for classification.