## 3 Results
### Comparative Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Class No.** & **DeepHyperX** & **ViT** & **SpectralFormer** & **SSFTT** & **MorphFormer** & **HybridSN** & **DBMA** & **DCTransformer** \\ \hline
1 & 67.27 & 52.10 & 47.18 & 91.26 & 92.56 & 94.07 & 94.71 & **94.72** \\
2 & 78.44 & 34.82 & 70.66 & 99.620 & 98.31 & 98.08 & 98.08 & **98.60** \\
3 & 83.15 & 75.54 & 77.17 & 100.00 & 100.00 & 100.00 & 100.00 & **100.00** \\
4 & 88.59 & 90.16 & 80.76 & **97.54** & 96.86 & 98.43 & 97.09 & 95.75 \\
5 & 90.10 & 72.45 & 76.47 & **99.57** & 99.43 & 99.28 & 99.56 & 99.43 \\
6 & 97.95 & 94.99 & 92.94 & 99.21 & 99.54 & 100.00 & 99.31 & **99.54** \\
7 & 66.99 & 65.14 & 69.39 & 94.01 & 87.91 & 92.81 & 90.30 & **94.44** \\
8 & 61.29 & 57.07 & 76.26 & 97.48 & 94.09 & 96.73 & 96.15 & **97.48** \\
9 & 57.45 & 36.88 & 66.49 & **96.10** & 93.97 & 90.60 & 91.31 & 86.17 \\
10 & 98.77 & 92.59 & 98.15 & 100.00 & 100.00 & 100.00 & 100.00 & **100.00** \\
11 & 92.60 & 78.94 & 91.40 & 97.03 & 98.39 & 98.63 & 97.58 & **98.64** \\
12 & 94.24 & 74.24 & 78.79 & 100.00 & 100.00 & 99.39 & 100.00 & **100.00** \\
13 & 93.33 & 97.78 & 93.33 & 97.28 & 100.00 & 100.00 & 99.13 & **100.00** \\
14 & 89.74 & 61.54 & 92.31 & 100.00 & 89.74 & 94.87 & 96.43 & **100.00** \\
15 & 99.98 & 100.00 & 100.00 & 97.11 & 100.00 & 100.00 & 100.00 & **100.00** \\
16 & 100.00 & 100.00 & 100.00 & 89.16 & 100.00 & 100.00 & 99.97 & **100.00** \\ \hline OA & 75.38 & 63.13 & 74.00 & 96.69 & 95.37 & 95.61 & 96.15 & **96.73** \\ AA & 85.00 & 74.02 & 81.96 & 95.10 & 96.93 & 96.68 & 97.12 & **97.80** \\ \(\kappa\) (\(\times\)100) & 72.02 & 58.46 & 70.21 & 93.36 & 94.69 & 95.10 & 95.63 & **96.25** \\ F1 score & 75.51 & 63.82 & 73.98 & 96.76 & 96.06 & 95.51 & 95.75 & **96.76** \\ precision & 76.71 & 66.73 & 75.23 & 96.85 & 96.25 & 95.58 & 95.74 & **96.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification performance (IN%) on the IP HSI dataset. Figure 5: Classification maps for the IP dataset: (**a**) pseudocolor image; (**b**) ground truth; (**c**) DeepHyperX; (**d**) ViT; (**e**) SpectralFormer; (**f**) SSFTT; (**g**) MorphFormer; (**h**) HybridSN; (**i**) DBMA; (**j**) DCTransformer. In Table 8, The MUUFL dataset reveals the results of our proposed DCTransformer model, which displays an OA of 94.46%, an AA of 82.86%, and kappa coefficients of 92.65%. The SSFIT, however, exhibits a remarkable performance, with an accuracy of 97.50% in class 8 and parameters of 0.22 MB and GFLOPs of 0.81. In comparison to our model, the SSFIT's performance has increased by 65% and 21%, as Table 9 demonstrates. Figure 8 presents 2D graphical representations illustrating the features extracted via t-SNE on the MUUFL dataset. In Figure 9, a visualization is provided, depicting the confusion matrix for the MUUFL dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Class No. ** & **DeepHyperX** & **ViT** & **SpectralFormer** & **SSFTT** & **MorphFormer** & **Hybrid5N** & **DBM** & **DCTransformer** \\ \hline
1 & 96.27 & 94.98 & 97.06 & 97.24 & **97.96** & 97.45 & 97.46 & 97.91 \\
2 & 78.53 & 72.26 &  
Figure 7: Visualization with confusion matrix for the Trento dataset: (**a**) DeepHyperX (93.97%); (**b**) ViT (88.07%); (**c**) SpectralFormer (91.16%); (**d**) SSFTT (96.40%); (**e**) MorphFormer (96.51%); (**f**) HybridSN; (**g**) DBMA; (**h**) DCTransformer (97.38%). Figure 8: Visualization with t-SNE for the MUUFL dataset: (**a**) DeepHyperX; (**b**) ViT; (**c**) SpectralFormer; (**d**) SSFIT; (**e**) MorphFormer; (**f**) HybridSN; (**g**) DBMA; (**h**) DCTransformer. Figure 9: Visualization with confusion matrix for the MUUFL dataset: (**a**) DeepHyperX (89.35%); (**b**) ViT (83.66%); (**c**) SpectralFormer (87.93%); (**d**) SSFTT (93.31%); (**e**) MorphFormer (93.81%); (**f**) HybridSN (93.39%); (**g**) DBMA (93.17%); (**h**) DCTransformer (94.31%). Table 9 exhibits the parameters and GFLOPs of the different methods compared. Through analysis of the parameters, the ViT embraces the least results yet has the worst performance on the AA of all datasets.