## 4 Experiment
### Different Fixed Size Slices and Visualization of Feature Maps  
#### 4.4.1 The Effect of Slicing Size on Classification Accuracy  
The category of the center pixel in the slice served as the label. In order to evaluate how the slice size influences the performance of the algorithm, we performed the experiment with slices  
Figure 8: (**a**–**d**) represent the classification results of the ground-truth, Dual-CNN, 6 Ch-CNN, and PauliRGB-CNN, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types** & **Names** & **Number of Classes** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{Classical} & Maximum Likelihood & 14 & 64.26 \\  & SVM & 14 & 71.29 \\  & Minimum Distance & 14 & 54.66 \\ \hline \multirow{3}{*}{Newly Published} & [PERSON] et al. [23] & 11 & 81.63 \\  & [PERSON] et al. [15] & 11 & 93.38 \\  & [PERSON] et al. [24] & 11 & 93.24 \\ \hline \multirow{2}{*}{Proposed} & \multirow{2}{*}{Dual-CNN} & 14 & 98.56 \\  & & 11 & 98.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results with other methods.  
of \(11\times 11,\,15\times 15\) and \(19\times 19\), which are subject to the span equalling to 5. The results of the classification are shown in Figures 9 and 10.  
As shown in Figure 9, the experiment which uses the slices of 11 \(\times\) 11 and 19 \(\times\) 19 has lower classification accuracy than that of using slices of 15 \(\times\) 15. Therefore, the slices of 15 \(\times\) 15 are appropriate for Flevoland full PolSAR data. As is shown in Figure 10a, for the slices of 11 \(\times\) 11, the Dual-CNN does not perform well for large area targets such as wheat (Wheat: brown; Wheat3: purple); and as is shown in Figure 10b, for the slices of 19 \(\times\) 19, the Dual-CNN does not perform well for small area targets such as stem bean, peas and so on. However, for the slices of 15 \(\times\) 15, as shown in Figure 8b, the Dual-CNN conducted on large or small area targets such as wheat or stem beans and peas is better than that of the other two methods. The slice size affects the Dual-CNN classification accuracy.  
If the size of the slice is too small, then the Dual-CNN learns inadequate feature information. Slices of large area targets, such as the wheat category, will contain the reduplicated information. Therefore, the features of large area targets learned by the Dual-CNN are not only small in number but also single, which leads to the low classification accuracy. As the size of slices is enlarged, the learning ability of the feature enhances, and the classification accuracy is improved. However, if the size of slices is too large, it will contain the extra features of other objects, which would cause the features of the small area targets to be submerged by other surrounding features. Thus, the Dual-CNN can extract many useless features of small area targets, and the accuracy of classification will decrease.  
Figure 10: (**a,c**) represent the classification results of the Dual-CNN with slices of 11 \(\times\) 11 and 19 \(\times\) 19 respectively; (**b,d**) display the false results in (**a,c**).  
Figure 9: The classification accuracy of the Dual-CNN with the slices of different sizes.