## 5 Discussion  
In this paper, experiments were conducted on the fusion framework of a GCN and CNN. In fact, the training accuracy of the present framework was able to converge to 100.00% on different categories. The comparison experiments were also able to achieve so on some categories. We reasonably attribute this to the fact that there are fewer publicly available datasets. As a result, the designed models are more relevant. In progressive model optimization, these models all achieve high accuracy classification results. The change in this phenomenon can be achieved in two ways: providing new datasets to increase the generality of the models or increasing the evaluation metrics for HSI classification. In the case of practical applications, the evaluation of the model can be performed in many ways, such as the speed of convergence and stability of the model. We further analyzed the performance of a CNN, GCN, and CNN+GCN (i.e., SFFNet). Table 8 shows the classification accuracies of single-frame and multi-frame fusions with different datasets when the niter is set to 300. As shown in Table 8, it can be seen that SFFNet basically achieves the optimal accuracy on each dataset. The AA accuracy of the CNN on the Indian Pines dataset is better than the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CLASSES** & **CLASSES-NAME** & **3D-CNN** & **WFCG** & **MDGCN** & **AMGCFN** & **SFFNet** \\ \hline
1 & Healthy grass & 95.12 \(\pm\) 0.82 & 99.19 \(\pm\) 4.03 & 99.59 \(\pm\) 2.89 & 79.20 \(\pm\) 0.15 & **100.00 \(\pm\) 0.00** \\
2 & Stressed grass & 99.61 \(\pm\) 0.64 & 85.49 \(\pm\) 3.56 & **100.00 \(\pm\) 0.00** & 60.50 \(\pm\) 0.14 & **100.00 \(\pm\) 0.00** \\
3 & Synthetic grass & 99.30 \(\pm\) 0.58 & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & 96.75 \(\pm\) 0.02 & **100.00 \(\pm\) 0.00** \\
4 & Trees & 94.94 \(\pm\) 0.58 & **100.00 \(\pm\) 0.00** & 99.22 \(\pm\) 2.47 & 67.15 \(\pm\) 0.23 & **100.00 \(\pm\) 0.00** \\
5 & Soil & 96.34 \(\pm\) 0.83 & **100.00 \(\pm\) 0.00** & 82.11 \(\pm\) 1.69 & 94.50 \(\pm\) 0.07 & **100.00 \(\pm\) 0.00** \\
6 & Water & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & 42.75 \(\pm\) 0.25 & **100.00 \(\pm\) 0.00** \\
7 & Residential & 96.58 \(\pm\) 5.03 & **100.00 \(\pm\) 0.00** & 97.01 \(\pm\) 3.08 & 56.51 \(\pm\) 0.12 & **100.00 \(\pm\) 0.00** \\
8 & Commercial & 95.31 \(\pm\) 3.78 & 99.22 \(\pm\) 5.41 & 96.88 \(\pm\) 4.57 & 35.14 \(\pm\) 0.14 & **99.61 \(\pm\) 0.04** \\
9 & Road & 65.89 \(\pm\) 1.83 & 85.05 \(\pm\) 2.16 & 93.46 \(\pm\) 3.44 & 65.01 \(\pm\) 0.12 & **99.53 \(\pm\) 0.07** \\
10 & Highway & 78.84 \(\pm\) 1.68 & 91.29 \(\pm\) 5.22 & 97.51 \(\pm\) 3.57 & 61.10 \(\pm\) 0.18 & **100.00 \(\pm\) 0.00** \\
11 & Railway & 79.51 \(\pm\) 3.02 & 98.77 \(\pm\) 3.01 & 91.39 \(\pm\) 2.68 & 61.07 \(\pm\) 0.11 & **99.59 \(\pm\) 0.01** \\
12 & Parking Lot 1 & 90.73 \(\pm\) 4.92 & 96.77 \(\pm\) 5.18 & 89.52 \(\pm\) 2.01 & 60.04 \(\pm\) 0.12 & **99.60 \(\pm\) 0.02** \\
13 & Parking Lot 2 & 93.02 \(\pm\) 4.23 & 96.51 \(\pm\) 4.06 & 95.35 \(\pm\) 3.21 & 65.25 \(\pm\) 0.31 & **98.84 \(\pm\) 0.02** \\
14 & Tennis Court & 86.49 \(\pm\) 0.43 & **100.00 \(\pm\) 0.00** & 98.65 \(\pm\) 2.51 & 90.34 \(\pm\) 0.12 & **100.00 \(\pm\) 0.00** \\
15 & Running Track & 87.02 \(\pm\) 0.43 & 98.47 \(\pm\) 1.46 & **100.00 \(\pm\) 0.00** & 87.20 \(\pm\) 0.20 & **100.00 \(\pm\) 0.00** \\ \hline OA & & 88.45 \(\pm\) 1.01 & 94.31 \(\pm\) 1.13 & 93.51 \(\pm\) 0.84 & 66.88 \(\pm\) 0.02 & **97.80 \(\pm\) 0.04** \\ AA & & 90.58 \(\pm\) 1.21 & 96.72 \(\pm\) 0.91 & 96.05 \(\pm\) 0.71 & 68.17 \(\pm\) 0.03 & **99.81 \(\pm\) 0.03** \\ kappa & & 89.49 \(\pm\) 1.18 & 95.96 \(\pm\) 1.03 & 95.08 \(\pm\) 0.88 & 64.21 \(\pm\) 0.03 & **99.82 \(\pm\) 0.03** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy of single models and dual models on different datasets.