## 4 Discussion
### Method Characteristic Analysis
#### 4.1.2 CNN's Use of Spatial Information  
The convolutional layer and the pooled layer are used alternately to simulate the working model of the human cerebral cortex through convolution and weight sharing. The deep features of the input target are learned and acquired, and the perception domain of the network is expanded, thereby fully utilizing the spatial features. Therefore, convolutional neural networks, as a classifier with a deep architecture, can reveal the deep features in buildings in PolSAR images where shallow layers' features cannot be found.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**E-SAR**} & \multicolumn{3}{c}{**GF-3**} & \multicolumn{3}{c}{**RADSAAT-2**} \\ \hline  & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** \\ \hline PauliRGB & 80.05 & 30.49 & 74.41 & 93.29 & 20.54 & 85.82 & 93.99 & 29.4 & 80.63 \\ PauliRGB + G\({}^{0}\) & **88.05** & **25.01** & **80.99** & **95.56** & **15.45** & **89.71** & **94.37** & **21.76** & **85.55** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Building extraction accuracy table using G\({}^{0}\) statistical texture parameters.  
Figure 8: Comparison of feature sets under three data using G\({}^{0}\) texture parameters before and after comparison. (**a,b**) is the experimental result under ESAR data, (**c,d**) is the experimental result under GF-3 data, and (**e,f**) is the experimental result under RADASAT-2 data. Where (**a,c,e**) is the experimental result of the input of only PauliRGB and (**b,d,f**) is the experimental result of the input texture parameter of PauliRGB and G\({}^{0}\).  
To verify that convolutional neural networks can effectively apply spatial information to PolSAR buildings, we use convolutional neural networks to compare the use of multi-layer perceptrons (MLP) for PolSAR building extraction. The difference between MLP and CNN is that the MLP is composed of a fully connected layer, and there is no convolution layer or pooling layer. The MLP cannot expand the receptive field and make full use of the spatial characteristics. We use a four-layer MLP with neurons of 500, 350, 200, and 84, using the feature set of PauliRGB and G\({}^{0}\) statistical texture parameters as input. The MLP and CNN building extraction effects are compared, and the results of that are shown in Figure 9.  
After using CNN to learn the spatial information of the building and obtain deeper features, the extraction effect of the building improves, the phenomenon of misclassification is reduced, and the outline of the building becomes accurate. In Table 5, compared with the use of MLP, the accuracy of CNN for building extraction increased by about 13%, the false alarm rate decreased by at least 17%, and the F1 parameter increased by at least 17%.  
Figure 9: Comparison of using the MLP and CNN methods. (**a**,**b**) is the experimental result under ESAR data, (**c**,**d**) is the experimental result under GF-3 data, and (**e**,**f**) is the experimental result under RADASAT-2 data. Where (**a**,**c**,**e**) is the experimental result of the MLP and (**b**,**d**,**f**) is the experimental result of the CNN.