## 4 Discussion
### Method Characteristic Analysis
#### 4.1.1 Combination of Polarization and Statistical Features  
PauliRGB is the most basic and commonly used method for characterizing PolSAR. However, using only PauliRGB to learn building features can only train the optimal feature information of buildings in PauliRGB images. In this case, the information in the PolSAR image is not sufficient. Further, there are some targets in PauliRGB images that are similar to the scattering mechanism and spatial structure of buildings. In the case of using only PauliRGB, they are easy to mistakenly divide into buildings, such as the parking lot, the street tree, etc., as shown in Figure 7.  
As unique and vital information in PolSAR data, statistical information can describe the heterogeneity of PolSAR. The statistical texture parameters can be used to interpret the statistical information of PolSAR features. The building area appears as a heterogeneous area in PolSAR. The different arrangement of the buildings results in different textures. In PolSAR images, the scattering mechanisms of different components of the same building may be different, so the building area contains essential context information. In view of the above situation, the G\({}^{0}\) distribution model can apply uniformly to them and contribute to building extraction. Furthermore, the use of CNN for PolSAR data classification has demonstrated that the introduction of appropriate features affects the classification of PolSAR. Through the G\({}^{0}\) statistical texture parameters under different data in the Figure 7c group, the G\({}^{0}\) statistical texture parameters can strengthen the characteristics of buildings and weaken the non-building features of buildings, such as the building features in Figure 7(A(d),B(d),E(d)) are enhanced, the street tree features in Figure 7 (B(d),D(d)) are adequately distinguished, and in C(d) and D(d), neatly arranged cars in the parking lot and roads, their differentiation from buildings has enhanced. Therefore, we add the G\({}^{0}\) statistical texture parameter to the input of CNN based on PauliRGB. The building extraction results are shown in Figure 7b,e,h. Combined with Figures 7 and 8, the introduction of G\({}^{0}\) statistical texture parameters on PauliRGB can improve the building extraction effect. By comparing the two groups in (e,f) of Figure 7, the outlines of the buildings in A(f) and E(f) become more accurate, and the results of the extraction are less misclassified. The street trees in B(f) and D(f) with similar spatial structures of buildings are more accurately distinguished, and the cars on the parking lot where the parts of C(f) had a misclassified result are accurately classified. According to the overall results in Figure 8, after adding the G\({}^{0}\) statistical texture parameters, the building extraction results of the three data indicate that the accuracy of the building profile is improved, the misclassification phenomenon is improved, and the non-building can be effectively distinguished, which has a similar scattering mechanism and spatial structure to buildings. Figure 7: Buildings and similar building features in the PolSAR image (**A**,**B**,**C**) images from ESAR. Group (**D**) images from GF-3 and Group (**E**) images from RADARSAT-2. Where (**a**) is the optical image, (**b**) is PauliRGB, (**c**) is the mask of the real building distributed on PauliRGB (red area), and (**d**) is the G\({}^{0}\) statistical texture parameter. (**e**) Classification results obtained by using only PauliRGB as CNN input training, in which red is a building, green is a non-building, and (**f**) is a classification obtained by adding a G\({}^{0}\) statistical texture parameter to PauliRGB as a CNN input training, in which red is a building and green is a non-building. Combined with Table 4, we find that after using the G\({}^{0}\) statistical texture parameters, the false alarm rate reduced by at least 5% and the evaluation parameter F1 increased by about 5% when the accuracy improved.