## 4 Experimental Results and Analysis
### Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**NO.** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** \\ \hline SSDT-net & **96.93** & **100** & **81.98** & **100** & **99.19** & **100** & **100** & **100** & **100** \\ SS3 FCN [19] & 92.36 & 92.58 & 66.35 & 98.13 & 95.63 & 99.30 & 99.43 & 69.27 & 99.67 \\ VHIS [29] & 85.91 & 73.88 & 33.72 & 65.92 & 46.42 & 79.63 & 73.59 & 72.16 & 71.87 \\ PCA/PCA-ON [35] & 95.88 & 90.06 & 47.1 & 79.71 & 66.76 & 79.84 & 79.62 & 78.14 & 94.24 \\ GAN/PCA-ON [35] & 96.02 & 86.1 & 50.76 & 79.73 & 68.45 & 79.73 & 79.67 & 78.02 & 94.03 \\
3 DCNN [34] & 96.49 & 75.15 & 39.89 & 61.61 & 52 & 79.21 & 76.81 & 74.84 & 78.14 \\ \hline
**C10** & **C11** & **C12** & **C13** & **C14** & **C15** & **C16** & **OA** & **AA** & **Kappa** \\ \hline
78.76 & 79.49 & **99.51** & **100** & **97.37** & 6 & **88.00** & **82.27 \(\pm\) 1.5** & **87.85 \(\pm\) 1.97** & **79.84 \(\pm\) 1.56** \\
84.07 & 85.31 & 97.98 & 98.45 & 87.32 & 52.31 & 59.97 & 81.32 & 86.13 & / \\
73.11 & 72.51 & 71.06 & 75.80 & 72.04 & 45.03 & 22.54 & 64.20 & 64.70 & / \\
**87.65** & 73.19 & 90.75 & 98.35 & 87.57 & 57.01 & 46.11 & 76.67 & 78.25 & / \\
85.72 & 70.95 & 89.08 & 96.93 & 85.54 & 54.66 & 43.87 & 75.87 & 77.45 & / \\
85.69 & 71.56 & 76.49 & 80.86 & 62.15 & **61.8** & 33 & 69.72 & 96.09 & / \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experimental results (%) of the Salinas Valley dataset. in Class 7 of 3 DCNN and VHIS are both 0. In our experiment, we selected 9.1% training samples for Class 7. The correct proportion of training samples also leads to effective clarification of our proposed method. Figure 7 visualizes the false-color image of the original HSI, their corresponding ground-truth map, and the classification results of the different models. Not only do the SSDT-net values improve in several metrics, but also the superiority of the algorithm can also be seen in Figure 7. In Figure 7b,e, it can be seen that the prediction map of SSDT-net is the most similar to the ground truth, especially for meadows. In some lower-right-corner areas, 1D-CNN and SS3 FCN misjudge the category of pixels for meadows. (3) Houston University Dataset: Compared to the other two datasets, the size of the Houston University dataset is larger (349 \(\times\) 1905). But the number of labeled samples is also limited. Table 5 presents the overall classification performance and class-specific performance by different methods of classification. The SSDT-net obtains the best result, achieving 85.97%, 83.89%, and 84.76% for the three overall classification metrics. The SAS branch can even fully classify specific classes. With the same training and testing data, our method displays higher accuracy by 6.04%, 5.89%, and 6.51% than SS3 FCN in terms of OA, AA, and Kappa. Figure 8 visualizes the false-color image of the original HSI, their corresponding ground-truth map, and the classification results of the different models. Our proposed method also obtained the best visual consistency with the ground truth. (4) Overall comparison: In Table 6, we show the overall performance between our method with the other techniques that do not interact with full pixel spectral information. It includes the SAS branch, SS3 FCN, 3D-CNN, VHIS, PCA/PAC-ON augmentation, and GAN/PCA-ON augmentation. Compared to single branches, dual branches obtained better performance. We suspect that the main reason is that the double branch implements the extraction and fusion of the proposed spectral correlation feature and classic spectral-spatial information. It also demonstrates that the abundant spectral information should be given more exploration in HSI classification.