## 4 Discussion  
### Influence of Label Ratio  
To evaluate the generalizability and robustness of the proposed FCGN and other methods, we set the number of training samples per class from 5 to 25 with an interval of 5. Figure 9 shows the OA obtained by the different methods on the three datasets. It can be observed that the proposed FCGN achieved a better classification accuracy than  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Class** & **3 DCNN** & **GCN** & **MiniGCN** & **HybirdSN** & **DBDA** & **MDGCN** & **FCGN** \\ \hline
1 & 88.31 & 98.64 & 96.19 & 99.34 & 99.62 & **100.00** & 99.74 \\
2 & 88.35 & 98.99 & 99.02 & 98.61 & 99.14 & **100.00** & **100.00** \\
3 & 82.01 & 73.84 & 86.32 & 94.38 & 97.45 & 99.16 & **100.00** \\
4 & 84.02 & 99.49 & 98.32 & 97.04 & 94.77 & **100.00** & 99.82 \\
5 & 87.76 & **99.66** & 96.35 & 98.24 & 98.02 & 94.32 & 97.71 \\
6 & 91.42 & 99.97 & 99.55 & 99.03 & **99.99** & 99.98 & 98.98 \\
7 & 90.94 & 93.54 & 98.54 & 96.89 & 97.62 & 98.85 & **99.99** \\
8 & 80.07 & **94.70** & 91.40 & 86.55 & 87.35 & 86.18 & 94.25 \\
9 & 94.88 & **100.00** & 99.74 & 99.12 & 89.37 & 99.97 & 99.97 \\
10 & 88.76 & 70.82 & 84.25 & 89.89 & 89.57 & 93.84 & **96.68** \\
11 & 83.62 & 80.85 & 83.51 & 91.23 & 90.32 & 98.29 & **99.01** \\
12 & 87.99 & 95.05 & 94.99 & 97.92 & 97.38 & 94.98 & **99.99** \\
13 & 72.15 & 94.94 & 89.47 & **99.46** & 98.99 & 97.00 & 99.36 \\
14 & 73.05 & 97.82 & 98.94 & 97.66 & 95.69 & 97.12 & **99.10** \\
15 & 91.34 & 54.25 & 67.39 & 81.47 & 86.77 & **95.92** & 94.56 \\
16 & 92.96 & 65.60 & 64.61 & **99.28** & 96.34 & 98.65 & 98.67 \\ \hline OA(\%) & 86.30 & 91.47 & 91.76 & 96.25 & 92.55 & 96.80 & **98.69** \\ AA(\%) & 86.10 & 90.92 & 90.53 & 95.38 & 94.90 & 97.14 & **98.61** \\ Kappa & 85.09 & 88.01 & 88.39 & 92.09 & 93.37 & 95.34 & **97.18** \\ Train time(s) & 153.09 & 269.04 & 1094.67 & 146.96 & 176.55 & 3377.41 & 1357.15 \\ Test time(s) & 93.37 & 23.02 & 57.47 & 72.72 & 120.17 & 65.33 & 42.46 \\ \hline \end{tabular}
\end{table}
Table 6: Classification results of the SA dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Class** & **3 DCNN** & **GCN** & **MiniGCN** & **HybirdSN** & **DBDA** & **MDGCN** & **FCGN** \\ \hline
3 & 69.77 & 69.19 & 86.13 & 82.40 & 90.32 & 86.81 & **97.35** \\
4 & 95.99 & 90.88 & 92.06 & 95.27 & **97.99** & 94.55 & 97.96 \\
5 & 91.30 & 94.27 & 95.11 & 95.77 & 99.01 & **99.76** & 99.73 \\
6 & 90.57 & 92.98 & 90.34 & 92.44 & 97.55 & **99.80** & 99.00 \\
7 & 80.21 & 82.81 & 88.99 & 89.06 & 94.37 & 98.07 & **99.89** \\
8 & 89.73 & 86.91 & 82.77 & 80.04 & 88.94 & 96.92 & **98.68** \\
9 & 91.12 & 95.55 & 92.87 & **99.11** & 98.39 & 98.38 & 98.41 \\ \hline OA(\%) & 86.33 & 85.41 & 89.67 & 92.99 & 97.22 & 98.22 & **98.99** \\ AA(\%) & 85.56 & 85.20 & 89.63 & 92.81 & 95.78 & 96.83 & **98.81** \\ Kappa & 85.21 & 80.37 & 87.09 & 89.98 & 96.72 & 97.27 & **97.90** \\ Train time(s) & 131.44 & 251.59 & 1058.37 & 122.61 & 145.88 & 3265.31 & 1283.37 \\ Test time(s) & 88.21 & 17.33 & 50.15 & 65.48 & 118.37 & 57.29 & 38.94 \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._other methods, and the classification accuracy of each method improved as the number of training samples increased, which proves the great robustness of the proposed FCGN.