## 4 Experiment
### Different Fixed Size Slices and Visualization of Feature Maps
#### 4.4.2 The Visualization of Feature Maps  
To better represent the Dual-CNN model, the 6 Ch-CNN and the PauliRGB-CNN are visualized in this paper. As is shown in Figure 11, the visualization processes of two branches are located at the convolution layer and max-pooling layer. The visualization contents include input data visualization, feature extraction visualization, and convolution kernel visualization. For input data visualization, two Lucerne slices of 15 \(\times\) 15, which come from the 6 Ch and Pauli RGB image in the same location, are selected as the samples to show the visualization process. Two slices are named as the 6 Ch-input and PauliRGB-input respectively. Figures 12-14 represent the visualization process of the 6 Ch-CNN, and Figures 15-17 depict the visualization process of the PauliRGB-CNN. Figures 12 and 15 show the visualized images of 6 Ch-input and PauliRGB-input; where Figures 12a and 15a denote the mixed visualized images of the 6 Ch-input and PauliRGB-input respectively, Figures 12b and 15b denote their unfolded visualized images in an individual channel. Since the data information of the 6 Ch-input and  
As shown in Figure 1, the feature extraction process is performed with three operations, i.e., convoluting, ReLu processing, and pooling processing. Figure 13 depicts the visualized image of the 6 Ch-input during extracting features where Figure 13a-c denote the visualization of the convolution operation, ReLu operation and max-pooling operation of the first round of feature extraction; and Figure 13d-f represent the visualization of the second round of feature extraction. As is shown in Figure 13a, the 15 \(\times\) 15 6 Ch-input serves as the input of the 6 Ch-CNN. Then, these data are processed by the 3 \(\times\) 3 convolution kernel in the first layer and are converted into a 14 \(\times\) 14 feature map as the output. Figure 13 shows the derived 12 visualized images. Compared with Figure 12b, the salient polarization features are preserved since 0 elements (light orange) in the 6 Ch-con1 are increased. Figure 13b illustrates the feature map of the 6 Ch-con1-reLu1 after ReLu operation; thus, most of the data are set to 0 (blue). By the max-pooling operation, the visualized feature map of 6 Ch-con1-reLu1-maxpooling1 after the first round of feature extraction can be obtained, as shown in Figure 13c. Although the polarization features are salient, there exists a lot of redundancy, so a second round of feature extraction is recommended. The processes are shown in Figure 13d-f. Figure 13f shows the final visualized feature map, where the red squares and the orange squares are the basic features of the input slices. The visualized feature maps of the convolution kernels in the two rounds of feature extraction are shown in Figure 14a,b. From the colorful block in Figure 14, it can be found that the elements of each convolution kernel are not all zero, which indicates that the Dual-CNN has been well trained and the obtained features are obvious. Figures 16 and 17 show the visualization of feature extraction and convolution kernels for the PauliRGB-input. It illustrates that the PauliRGB-input requires a second round of feature extraction to extract more abstract spatial features as done in the 6 Ch-input. Figure 16: Visualized feature maps of the PauliRGB-input. (**a**,**c**,**e**) denote the visualized feature maps of the convolution operation, ReLu operation and max-pooling operation in the first round of feature extraction; (**b**,**d**,**f**) denote the visualization in the second round of feature extraction. Figure 14: Visualization of the convolution kernel of the 6 Ch-CNN: (**a**) visualization of the convolution kernel in the first round of feature extraction; and (**b**) visualization of the convolution kernel in the second round of feature extraction. Figure 15: (**a**) Mixed visualized image of the PauliRGB-input; (**b**) Unfolded visualized images of the PauliRGB-input. Note that the features extracted by the 6 Ch-CNN and PauliRGB-CNN have polarimetric and spatial characteristics.