## 4 Experimental Results and Analysis
### Classification Results  
The overall accuracy (OA), the average accuracy (AA), and the Kappa coefficient (K) are employed to quantitatively evaluate the classification performance. Higher values indicate better classification accuracy. To demonstrate the superiority of the techniques in this paper, we compare them with the state-of-the-art methods, including VHIS [29], 3 DCNN [34], SS3 FCN [19], PCA/PCA-ON [35], and GAN/PCA-ON [35]. (1) Salinas Valley Dataset: The experimental results of the Salina dataset are collected in Table 3, and the best performance indicators are bolded. Compared to other methods, as seen in Table 3, our method was confirmed to achieve the best result with OA, AA,  
Figure 8: Prediction maps of different models for Houston University dataset. (**a**) False-color image; (**b**) Ground truth; (**c**) 1D-CNN; (**d**) SS3 FCN; (**e**) SSDT-net (OA = 85.43%, AA = 83.55%). and Kappa of 82.24%, 87.85%, and 79.84%, respectively. Our method is consistent with that of SS3 FCN [19], which is based on pixel-based classification rather than patch-based classification. Based on the SAS branch, we added the SFCI branch, and the OA, AA, and Kappa increased from 78.06%, 82.71%, and 75.31% to 82.27%, 87.85%, and 79.84%. This also confirms the role of dual branch for classification. In addition, we also clearly list the classification accuracy values of each class in Table 4, most of which are higher than those of other methods. Figure 6 visualizes the false-color image of the original HSI and their corresponding ground-truth map, as well as the classification results of the different models. It can be seen from Figure 6c-e that only using spectral features for classification produces many noise points. The combination of 3D and 1D-CNN, such as SS3 FCN, makes the classification effect improved. But some specific categories did not perform well in the classification, such as fallow_plow and corn weeds. Based on 3D-CNN, our method used full channel information interaction, which shows a better classification effect. (2) Pavia University dataset: Table 4 demonstrates the experimental results of several competing methods on the Pavia University dataset, and the best performance indicators are bolded. The OA, AA, and Kappa of our proposed method are 83.81%, 81.55%, and 73.67%, respectively. Compared to the state-of-the-art comparison algorithm SS3 FCN, we obtained an about 4% classification performance improvement in both OA and AA metrics. Specifically for each class of objectives in the database, we obtained optimal performance on six of the nine classes of objectives. Furthermore, we can find that the accuracy indicators \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **3 DCNN [34]** & **VHIS [29]** &
\begin{tabular}{c} **SS3 FCN-** \\ **VHIS** \\ \end{tabular} & **SS3 FCN [19]** & **SSDT-net** \\ \hline C1 & 90.66 & 93.40 & 85.19 & 97.48 & **98.02** \\ C2 & 81.85 & 86.20 & 90.40 & 90.86 & **92.01** \\ C3 & 41.92 & 47.58 & 14.83 & **58.75** & 56.23 \\ C4 & 93.02 & 86.89 & 92.98 & 84.81 & **98.02** \\ C5 & 53.79 & 59.81 & 99.37 & 94.82 & **99.58** \\ C6 & 25.20 & 27.14 & 17.63 & 23.59 & **49.93** \\ C7 & 0 & 0 & 46.35 & 61.61 & **72.66** \\ C8 & 70.18 & 78.46 & **89.59** & 88.84 & 87.00 \\ C9 & 79.03 & 79.27 & **99.06** & 88.68 & 78.77 \\ \hline OA & 70.07 & 73.26 & 76.23 & 79.89 & **83.81 \(\pm\) 0.96** \\ AA & 60.18 & 62.08 & 70.60 & 76.60 & **81.55 \(\pm\) 1.9** \\ Kappa & / & / & / & / & 73.67 \(\pm\) 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The experimental results (%) on the Pavia University dataset.