## 3 Results

Table 6: UA (%) for different wetland types using different input feature sets and supervised classification methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Wetland** & **Saltwater** & **Farmland** & **River** & **Shrub** & **Grass** & **Suaeda salsa** & **Tidal Flat** \\ \hline GF-3 ML & 43.6 & 64.26 & 1.6 & 65.86 & 17.26 & 35.48 & 82.5 \\ GF-3 MD & 61.74 & 61.41 & 61.43 & 65.5 & 37.11 & 29.03 & 42.88 \\ GF-3 SVM & 93.97 & 74.57 & 0.62 & 56.44 & 46.38 & 0 & 78.81 \\ \hline OHS ML & 99.05 & 99.57 & 99.64 & 92.54 & 89.95 & 83.06 & 91.99 \\ OHS MD & 87.3 & 98.09 & 93.4 & 87.38 & 76.7 & 71.77 & 85.28 \\ OHS SVM & 98.9 & 99.47 & 97.08 & 92.99 & 93.78 & 80.65 & 88.03 \\ \hline GF-3+OHS ML & 99.03 & 98.94 & 98.99 & 91.89 & 91.05 & 68.55 & 94.96 \\ GF-3+OHS MD & 93.31 & 96.23 & 96.05 & 74.08 & 67.01 & 74.19 & 82.98 \\ GF-3+OHS SVM & 99.05 & 98.5 & 95.69 & 95 & 93.7 & 81.45 & 93.79 \\ \hline \end{tabular}
\end{table} 
Table 5: PA (%) for different wetland types using different input feature sets and supervised classification methods. Figure 9: The overall accuracy (OA) and Kappa coefficient obtained by ML, MD, and SVM methods for GF-3, OHS, and synergetic data sets. Considering that the synergetic technique essentially combines the structural and dielectric information of wetland types with the scattered power components, GF-3 and OHS data are transformed into more meaningful target information content than the GF-3 or OHS data alone. Therefore, we found that the accuracy metrics of synergetic classification were significantly improved compared with the single data classification in Table 4 and Figure 9. Among the three tested classifiers, the MD method provides the lowest synergetic classification accuracy of 89%, and the other two methods (ML and SVM) are relatively close, with an overall accuracy of 97% and a Kappa coefficient of 0.96. In addition to the OA and Kappa coefficients (Table 4 and Figure 9) and corresponding classification images (Figure 8), the PA, UA, and F1-score were calculated according to the confusion matrix (Tables 5-7 and Figure 10). Concerning the values of PA, UA, and \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Wetland** & **Saltwater** & **Farmland** & **River** & **Shrub** & **Grass** & **_Suueda salsa_ & **Tidal Flat** \\ \hline GF-3 ML & 59.05 & 59.89 & 2.37 & 56.15 & 26.64 & 1.87 & 52.05 \\ GF-3 MD & 74.04 & 59.23 & 26.22 & 55.71 & 38.95 & 0.87 & 43.39 \\ GF-3 SVM & 90.99 & 65.50 & 1.11 & 55.78 & 52.50 & 0.00 & 83.12 \\ \hline OHS ML & 99.51 & 91.61 & 92.46 & 91.90 & 92.89 & 66.45 & 95.80 \\ OHS MD & 93.22 & 93.65 & 62.59 & 83.24 & 84.20 & 2.75 & 91.41 \\ OHS SVM & 99.36 & 90.16 & 90.17 & 95.74 & 95.10 & 27.44 & 93.55 \\ \hline GF-3+OHS ML & 99.48 & 93.74 & 93.46 & 90.81 & 92.93 & 70.54 & 97.18 \\ GF-3+OHS MD & 96.53 & 96.00 & 62.85 & 68.55 & 68.56 & 5.08 & 89.79 \\ GF-3+OHS SVM & 99.39 & 94.28 & 90.53 & 97.44 & 92.92 & 37.62 & 96.72 \\ \hline \end{tabular}
\end{table} 
Table 7: F1-score (%) for different wetland types using different input feature sets and supervised classification methods. Figure 11: The confusion matrixes obtained by ML, MD, and SVM methods for synergetic data sets. (a) GF-3 and OHS ML, (b) GF-3 and OHS MD, (c) GF-3 and OHS SVM. Figure 10: The PA (a), UA (b), and F1-score (c) obtained by ML, MD, and SVM methods for GF-3, OHS, and synergetic data sets. F1-score obtained for each wetland type, the best classified types are saltwater, farmland, river, and tidal flat, with values above 80%. The accuracy of _Suaeda salsa_ was the lowest, mainly due to the fact that _Suaeda salsa_ is small in size (approximately 1 m in height and width) and sparsely distributed on the tidal flat, whereas the image resolution of 10 m was used in this study. The PA, UA, and F1-score of saltwater and river for GF-3 data are significantly lower than those of the other two datasets.