## 3 Results
### Ablation Experiments  
By employing the MUUFL dataset, we conducted ablation experiments to evaluate the potency of the two modules we had introduced. Table 10 displays the experimental results for different cases. The DFE and BFE models we proposed have been removed in the network of DBFFS, and this structure is used as the baseline. The version without the DFE model is denoted as w/o DFE and maintains the same remaining architecture and conditions. Similarly, we designed the experiment by discarding the BFE model, which is named w/o BFE. By analyzing the above results of ablation experiments, the effectiveness of the DFE and the BFE is significantly reflected in the AA metrics. With only a minimal parameter increase, the classification performance leaps greatly. It is worth noting that the DBFFS suffers from the adverse effect of overfitting, although we have cleverly designed the dual-branch structure and fully utilized lightweight technology, which is also present in universal models. When the special models have been removed, the overfitting will be mitigated, and the model will be trained better, supposing the epoch and learning rate are set consistently. It makes the experiment results increase in a rational range and affects the significance of the results of ablation experiments, such as in OA and kappa metrics. This does not mean those models have a leak effectiveness and are unnecessary; when comparing the accuracy of the baseline and DBPSS, the results slump in three metrics we adopted due to capturing few spatial-spectral features. To conclude, further research should concentrate on optimizing the structure and augmenting the effectiveness of feature extraction modules.  
The different window sizes are investigated thoroughly in this paper. Table 11 exhibits the impact of the window size of the input HSI data for the classification task. We can deduce from the table that as the window size expands, both the computational complexity and the training time increase. Additionally, the average accuracy initially ascends and subsequently declines. The reason is that, with more neighboring pixels, the DFE with a CNN may work better due to encoding more context information. Still, the BFE with the DCT has a constant number of frequency components, which hardly adapt to the window size increases. After considering achieving a good trade-off between the three evaluation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cases** & **OA (\%)** & **AA (\%)** & **Kappa (\%)** & **Params (MB)** \\ \hline baseline & 93.83 & 80.39 & 91.83 & 0.12 \\ w/o DFE & 94.26 & 81.75 & 92.40 & 0.165 \\ w/o BFE & 94.04 & 80.54 & 92.10 & 0.13 \\ DCTransformer & 94.46 & 82.86 & 92.65 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ablation experiment on the MUUFL dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Params (MB)** & **GFLOPs** \\ \hline DeepHyperX & 0.20 & 2.65 \\ ViT & 0.08 & 0.41 \\ SpectralFormer & 0.03 & 0.51 \\ SSFTT & 0.28 & 0.98 \\ MorphFormer & 0.22 & 0.72 \\ HydridSN & 0.16 & 1.53 \\ DBMA & 0.23 & 0.97 \\ DCTransformer & 0.17 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Parameters and FLOPS of all methods on the MUUFL dataset.  
metrics and the computational volume, we ultimately chose a window size of \(11\times 11\) for all datasets. Figure 10 shows the OA, AA, and kappa for the different window size cases. When a patch size of \(13\times 13\) is set, our model achieves the best performance, yet it brings an additional and undeniable increase in GFLOPs.