## 2 Datasets and Methods
### Methods
#### 2.3.1 GF-3 Preprocessing  
As shown in Figure 3, GF-3 PolSAR image processing consists of image preprocessing, features extraction, image classification, and accuracy evaluation. Figure 3: The overall technical flow chart of this study. First, the preprocessing of the original PolSAR image in single look complex (SLC) format was performed with Pixel Information Expert SAR (PIE-SAR(r)) 6.0 and ENVI(r) 5.6, including radiometric calibration, polarization filtering, and polarization matrix conversion. After importing the GF-3 full-polarization SAR data, the radiometric correction process can be completed automatically. A polarized scattering matrix can only describe so-called coherent or pure scatterers, whereas distributed scatterers usually use second-order descriptors [54]. Therefore, after importing the data, the polarization scattering matrix was converted into the polarization covariance matrix or polarization coherence matrix by means of a transformation function. PolSAR image speckle noise seriously affects the image quality, accuracy of landcover information extraction, and ground object interpretation. Azimuth and range multi-looking of 3*3 and the refined Lee filter with the window size of 3 \(\times\) 3 were employed to reduce speckle noise with the output image grid size of 8 m.  
Feature extraction is divided into two steps. The first step is polarization decomposition, which aims to effectively separate ground objects dominated by different scattering mechanisms. Polarization features derived from polarization decomposition can reveal the scattering mechanism of the ground object to determine the type. For example, surface scattering is dominant in water bodies, whereas secondary scattering and volume scattering are dominant in residential land and forest, respectively. The polarization decomposition was carried by the H-A-\(\alpha\) decomposition method and the three-component Freeman decomposition method, respectively [23]. H-A-\(\alpha\) decomposition uses the scattering matrix transformation to obtain the coherency matrix [T3], where [T3] is a semi-positive definite Hermite matrix [31]. The three second-order parameters of H-A-\(\alpha\) decomposition are the eigenvalues and eigenvector functions of [T3], which are defined as follows [32]:  
* entropy \(H\): \[H=-\sum\limits_{k=1}^{3}P_{k}\log_{3}(P_{k})\] (1)  
In the formula, \(P_{1}=\lambda_{i}/\sum\limits_{k=1}^{3}\lambda_{k}\), \(\sum\limits_{k=1}^{3}P_{i}=1\). Entropy \(H\) reflects the randomness of the target scattering mechanism. For example, a low \(H\) indicates that only one scattering mechanism is dominant, whereas a high \(H\) indicates more than two primary scattering mechanisms. * alpha \(\alpha\): \[\alpha=P_{1}\alpha_{1}+P_{2}\alpha_{2}+P_{3}a_{3}\] (2)  
In the formula, the magnitude of \(\alpha_{1}\), \(\alpha_{2}\)and \(\alpha_{3}\) indicates the primary scattering mechanism: surface scattering, secondary scattering, and volume scattering; \(\alpha\) denotes the scattering angle. When \(\alpha\) is close to 0, it indicates that only one scattering mechanism exists. In contrast, a larger \(\alpha\) value (maximum 90 degrees) indicates a more complex surface scattering mechanism. * anisotropy \(A\): \[A=\frac{\lambda_{2}-\lambda_{3}}{\lambda_{2}+\lambda_{3}}\] (3)  
In the formula, \(\lambda_{i}\) is the eigenvalue of the coherency matrix [T3]. Anisotropy reflects the relationship between two smaller scattering mechanisms. High \(A\) represents that two scattering mechanisms are dominant simultaneously, whereas low values of \(A\) and \(H\) show that only one scattering mechanism is dominant. However, low \(A\) and high \(H\) indicate that three scattering mechanisms are similar, and the scattering is almost random. Therefore, the polarization scattering information of ground objects can be fully used to distinguish the surface types effectively. Figure 4 roughly shows the general distribution of wetlands in the YRD.