## 3 Experiments
Next, we analyze the statistical property of the GF-3 polarimetric SAR data. The Wishart classifier [16] is a very classic classifier, which has been proved to be effective for many imaging scenarios and Earth observing missions. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \(\mathbf{u}\pm\mathbf{\sigma}\) & **Ocean** & **Forest** & **Urban** & **Tilted Urban** \\ \hline \multirow{2}{*}{\(H\)} & The GF-3 data & 0.22 \(\pm\) 0.00 & 0.80 \(\pm\) 0.01 & 0.57 \(\pm\) 0.02 & 0.78 \(\pm\) 0.02 \\  & The Radarsat-2 data & 0.23 \(\pm\) 0.00 & 0.83 \(\pm\) 0.01 & 0.62 \(\pm\) 0.02 & 0.81 \(\pm\) 0.02 \\ \hline \multirow{2}{*}{\(alpha(^{\circ})\)} & The GF-3 data & 8.77 \(\pm\) 1.94 & 40.07 \(\pm\) 6.78 & 42.25 \(\pm\) 8.37 & 46.46 \(\pm\) 8.40 \\  & The Radarsat-2 data & 9.93 \(\pm\) 3.06 & 42.94 \(\pm\) 5.87 & 50.00 \(\pm\) 9.43 & 51.87 \(\pm\) 7.67 \\ \hline \multirow{2}{*}{\(\Delta\alpha_{\rm B}(^{\circ})\)} & The GF-3 data & 3.95 \(\pm\) 0.93 & 29.60 \(\pm\) 11.36 & 9.82 \(\pm\) 20.55 & 16.72 \(\pm\) 22.28 \\  & The Radarsat-2 data & 4.00 \(\pm\) 1.84 & 30.56 \(\pm\) 14.20 & -3.82 \(\pm\) 22.00 & 7.06 \(\pm\) 24.42 \\ \hline \multirow{2}{*}{\(\alpha_{\rm B}(^{\circ})\)} & The GF-3 data & 4.19 \(\pm\) 0.97 & 36.22 \(\pm\) 8.84 & 41.20 \(\pm\) 12.25 & 46.60 \(\pm\) 12.41 \\  & The Radarsat-2 data & 4.39 \(\pm\) 1.93 & 40.41 \(\pm\) 9.32 & 52.77 \(\pm\) 14.39 & 55.75 \(\pm\) 12.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The averaged values of different parameters of the four selected typical theme areas, where \(u\) denotes the averaged value and \(\sigma\) denotes the standard deviation. Figure 5: The Wishart classifier results initialized by the \(H\)/\(alpha\) plane are shown in (**a**,**c**), and results initialized by the \(\Delta\alpha_{\rm B}\)/\(\alpha_{\rm B}\) diagram are shown in (**b**,**d**). (**a**,**b**) are results of the GF-3 data, and (**c**,**d**) are results of the Radarsat-2 data. Before applying the iterative Wishart classifier, initial classification is needed. Both the \(H/alpha\) and \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) classification results can be taken as initializations. Iteration of the Wishart classifier is set to stop when the total number of pixels switching between classes is less than 1% of the total number of pixels. The final results are shown in Figure 5. It is observed that results of the GF-3 data have a clear classified ocean surface. Since after the iteration pixels classified into class \(i(i=1\ldots 9)\). No longer correspond to the \(i\)-th zone of the scattering diagrams, we assume that in each typical scattering theme, the class with the maximum classified pixel number is selected as the correct classification labels for this area. After analyzing, we found that iterative results from zone 9 accounts for a significant number of pixels for ocean surface. Iterative results from zone 6 and zone 2 take up majority pixels for the forested area. Iterative results from zone 8 and zone 4 contribute a large proportion of pixels for the urban area, and iterative results from zone 1 accounts for majority pixels for the tilted urban area. By using this classification division, the confusion matrices for both GF-3 and Radarsat-2 data sets are shown in Table 3. It is observed that when applying the iterative Wishart classifier results of the GF-3 data and the Radarsat-2 data produce similar overall accuracies. This shows that when considering the statistical property in terrain classification, Radarsat-2 data and GF-3 data have similar performance. Since the two data sets were collected on different dates, this kind of small difference may be caused by disturbance of climate effects. For both data sets, the result initialized by the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\). diagram slightly outperforms that by the \(H/alpha\). initialization when the same convergence condition is applied.