## 4 Experimental Discussion
### Accuracy Analysis
#### 4.2.2 Results on the Pavia University Scene Dataset  
As can be seen from the comparison in Figure 9, the classification map generated by SFFNet is closer to the real ground value. Compared with the other frameworks combining CNN and GCN, SFFNet first uses a CNN to extract features and then constructs a graph data structure for the GCN classification, which can better capture image information and spatial relationships. This method can successfully complete the task of category differentiation with large jumps. Figure 8: Classification results on the Indian Pines dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CLASSES** & **CLASSES-NAME** & **3D-CNN** & **WFCG** & **MDGCN** & **AMGCFN** & **SFFNet** \\ \hline
1 & Alfafafa & \(\mathbf{100.00\pm 0.00}\) & \(88.89\pm 1.45\) & \(77.78\pm 1.32\) & \(69.20\pm 0.18\) & \(88.89\pm 1.11\) \\
2 & Corn-notill & \(95.41\pm 4.89\) & \(96.39\pm 4.62\) & \(95.41\pm 1.04\) & \(90.68\pm 0.03\) & \(\mathbf{99.67\pm 0.03}\) \\
3 & Corn-mintill & \(99.38\pm 3.66\) & \(83.75\pm 2.01\) & \(67.50\pm 0.21\) & \(90.82\pm 0.07\) & \(\mathbf{100.00\pm 0.00}\) \\
4 & Corn & \(\mathbf{100.00\pm 0.00}\) & \(87.23\pm 2.55\) & \(91.49\pm 0.14\) & \(89.71\pm 0.09\) & \(95.74\pm 2.01\) \\
5 & Grass-pasture & \(\mathbf{100.00\pm 0.00}\) & \(98.75\pm 3.68\) & \(\mathbf{100.00\pm 0.00}\) & \(85.78\pm 0.05\) & \(\mathbf{100.00\pm 0.00}\) \\
6 & Grass-trees & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(96.34\pm 0.03\) & \(\mathbf{100.00\pm 0.00}\) \\
7 & Grass-pasture-mowed & \(80.00\pm 9.02\) & \(\mathbf{100.00\pm 0.00}\) & \(80.00\pm 2.35\) & \(88.22\pm 0.11\) & \(\mathbf{100.00\pm 0.00}\) \\
8 & Hay-windrowed & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(95.92\pm 0.03\) & \(99.62\pm 0.00\) & \(\mathbf{100.00\pm 0.00}\) \\
9 & Oats & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(85.71\pm 1.65\) & \(99.47\pm 0.02\) & \(\mathbf{100.00\pm 0.00}\) \\
10 & Soybean-notill & \(87.43\pm 1.62\) & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(89.37\pm 0.05\) & \(\mathbf{100.00\pm 0.00}\) \\
11 & Soybean-mintill & \(51.85\pm 3.41\) & \(98.91\pm 3.16\) & \(99.13\pm 1.32\) & \(95.01\pm 0.03\) & \(\mathbf{100.00\pm 0.00}\) \\
12 & Soybean-clean & \(86.55\pm 2.34\) & \(68.07\pm 2.34\) & \(89.08\pm 2.27\) & \(83.16\pm 0.05\) & \(\mathbf{94.96\pm 1.72}\) \\
13 & Wheat & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(93.63\pm 0.07\) & \(\mathbf{100.00\pm 0.00}\) \\
14 & Woods & \(81.30\pm 2.54\) & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(98.29\pm 0.01\) & \(\mathbf{100.00\pm 0.00}\) \\
15 & Buildings-Grass-Trees- & \(91.80\pm 4.02\) & \(\mathbf{100.00\pm 0.00}\) & \(86.89\pm 0.03\) & \(92.01\pm 0.07\) & \(\mathbf{100.00\pm 0.00}\) \\
16 & Stone-Steel-Towers & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(\mathbf{100.00\pm 0.00}\) & \(88.74\pm 0.13\) & \(\mathbf{100.00\pm 0.00}\) \\ \hline OA & & \(83.55\pm 1.65\) & \(95.55\pm 0.63\) & \(94.79\pm 0.42\) & \(92.68\pm 0.01\) & \(\mathbf{99.49\pm 0.41}\) \\ AA & & \(92.11\pm 1.98\) & \(95.12\pm 0.41\) & \(91.81\pm 0.25\) & \(90.63\pm 0.02\) & \(\mathbf{98.70\pm 0.10}\) \\ kappa & & \(81.52\pm 1.86\) & \(94.91\pm 0.88\) & \(94.03\pm 0.54\) & \(91.67\pm 0.01\) & \(\mathbf{99.42\pm 0.05}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy (%) on the Indian Pines dataset of the different methods. Table 6 shows the classification results of the different frameworks on the PU dataset. As can be seen from the optimal results, the classification performance of SFFNet is significantly better than that of other competitors. SFFNet is able to better extract the data features and construct a more appropriate graph structure, which not only takes into account its local information, but also flexibly extracts the global information. It efficiently perceives the changes in spatial structure in order to meet the classification requirements.