## 4 Conclusions  
In this study, the parameters \(\alpha_{\rm B}\) and \(\Delta\alpha_{\rm B}\), proposed based on the ratio of the co-polarization parameters under reflection symmetric case, was analyzed under the reflection asymmetry case. Then, both the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) diagram and the \(H/alpha\) method, together with the statistical Wishart classifier, are applied to analyze the performances of GF-3 polarimetric SAR data for target physical scattering mechanism interpretation and terrain classification. The \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) diagram intrinsically integrates the co-polarization phase difference to discriminate between target scattering mechanisms and thus performs better than the classic \(H/alpha\) method. On average, the scattering mechanism identification accuracy of the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) method is about 3-5% higher than that of the \(H/alpha\) method by using the test data. The polarization entropy \(H\) shows similar performance with the GF-3 data and the Radarsat-2 data. However, the polarization \(alpha\) performs poorly with the GF-3 data. Results show that the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) diagram is effective in interpreting the physical scattering mechanism for GF-3 data. When using the classic Wishart classifier for terrain type classification, the GF-3 data and the Radarsat-2 data give similar classification accuracies. Further, GF-3 data shows its advantages for ocean surface  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **\%** & **Ocean** & **Forest** & **Urban** & **Tilted Urban** & **Others** & **Overall Accuracy** \\ \hline \multirow{3}{*}{(GF-3 data)} & Ocean & 100 & 0 & 0 & 0 & 0 & \\  & Forest & 0.25 & 96.48 & 0.90 & 1.08 & 1.29 & \\ \(H/alpha\)-Wishart & Urban & 0.01 & 7.01 & 66.92 & 0.40 & 25.66 & \\  & Tilted urban & 0 & 29.33 & 3.04 & 58.53 & 9.11 & \\ \hline \multirow{3}{*}{(GF-3 data)} & Water & 100 & 0 & 0 & 0 & 0 & \\  & Field & 0.25 & 96.47 & 0.91 & 1.09 & 1.28 & \\ \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\)-Wishart & Forest & 0.01 & 7.00 & 67.10 & 0.40 & 25.49 & \\  & Urban & 0 & 27.27 & 3.04 & 60.56 & 9.13 & \\ \hline \multirow{3}{*}{(Radarsat-2 data)} & Water & 99.16 & 0 & 0 & 0 & 0.84 & \\  & Field & 0.07 & 88.96 & 1.01 & 4.31 & 5.65 & \\ \(H/alpha\)-Wishart & Forest & 0 & 2.42 & 75.96 & 0.76 & 20.28 & \\ \cline{1-1}  & Urban & 0 & 14.06 & 3.51 & 66.14 & 16.29 & \\ \hline \multirow{3}{*}{(Radarsat-2 data)} & Water & 99.18 & 0 & 0 & 0 & 0.82 & \\ \cline{1-1}  & Field & 0.07 & 88.65 & 1.04 & 4.31 & 5.92 & \\ \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\)-Wishart & Forest & 0 & 2.32 & 76.82 & 0.76 & 20.11 & \\ \cline{1-1}  & Urban & 0 & 13.83 & 3.54 & 66.17 & 16.46 & \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the GF-3 and the Radarsat-2 data classification results by using the Wishart classifier.  
monitoring. In future works, we will focus on the development of the statistical model-based classifier such that the GF-3 data can be efficiently used.  
This work was partly supported by NSFC under Grant No. 61771043, partly by the major research plan of NSFC under Grant No. 61490693, partly by the fundamental research funds for the central universities under Grant No. FRF-TP-15-090A1, and partly by the major project of high resolution earth observation systems under Grant No. 30-Y20A12-9004-15/16, 03-Y20A10-9001-15/16, and 41-Y20A14-9001-15/16.  
[PERSON] and [PERSON] proposed the new method; [PERSON] wrote the paper and performed the experiments; [PERSON] provided the data and designed the experiments.  
The authors declare no conflict of interest.