## 4 Results
### Comparison with Other Methods  
To demonstrate the performance of our proposed MSTCC, we selected four methods for qualitative comparison on three datasets and quantitative comparison on two datasets. The hyperparameter settings were the same as those previously used. The learning rate was initialized at \(1\times 10^{-4}\) and decayed by a factor of 0.9 after every 100 epochs. The total epoch on the datasets was 500.  
The first experiment was reported on the IP dataset. We roughly set 50 labelled pixels in each land-cover category for training and the rest for testing. The OA and Kappa coefficient are shown in Table 5. Our proposed method achieved the best performance compared to the other four methods. In particular, the MSTCC demonstrated a 5.2% increase in OA and a 5.6% increase in the Kappa coefficient compared with CNN-3D, which performed better than CNN-2D. Furthermore, the performance of the MSTCC was also better than that of the FCN-ELM, which performed well on the IP dataset as a CNN-based method. For the transformer-based methods, SpectralFormer and our proposed method achieved better accuracy than the CNN methods, where the former improved by 1.8% OA and 0.8% Kappa coefficient.  
The second experiment evaluated the performance of the compared methods on the PU dataset, shown in Table 6. Our MSTCC method performed better than all the state-of-the-art methods. It was 1.5% higher for OA with a 0.9% higher Kappa coefficient than the SpectralFormer, and much higher than the other CNN-based methods. Therefore, it can be inferred that our method more effectively identified all the land-cover categories compared to the CNN-based methods. The proposed components can thus increase the accuracy of the base transformer classifier.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **IP** & **PU** & **PC** \\ \hline Baseline & 89.3 & 92.5 & 95.8 \\ Input Mask & 89.9 & 91.8 & 96.5 \\ CCB Module & 91.7 & 94.6 & 96.0 \\ Input Mask + CCB Module & **91.9** & **94.9** & **96.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification accuracy (%) of the two proposed components for the ablation study with the OA on the IP dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **CNN-2D** & **CNN-3D** & **FCN-ELM** & **SpectralFormer** & **MSTCC (Ours)** \\ \hline
1 & 83.1 & 81.7 & 60.1 & 70.5 & 84.2 \\
2 & 74.7 & 86.5 & 88.4 & 82.1 & 80.7 \\
3 & 94.2 & 93.6 & 76.9 & 90.2 & 88.6 \\
4 & 81.4 & 80.6 & 89.1 & 95.6 & 97.  
The third experiment's results are shown in Table 7. Similar to the above two datasets, our method achieved the best performance on the PC dataset. Both the OA and Kappa coefficient were higher than 95%. Each class of the dataset was more accurately classified than the other CNN-based methods. The OA result was 0.6% higher than the SpectralFormer, while the Kappa coefficient was 0.9%.  
For the qualitative evaluation, we selected all the methods and visualised them on the IP dataset and PU dataset, as shown in Figures 4 and 5. The results of our MSTCC methods are smooth and clear, performing better than other methods, especially the CNN-based methods. Thus, we can deduce that CCB's proficient capacity to capture distinctive attention features contributes to the successful classification of mixed pixels located near class borders. The visualization results of the CCB module and input mask have a finer