## 3 Experiments and Discussion
### Parameter Analysis
#### 3.3.5 Effectiveness of Attention Mechanism  
Experiments without the use of the attention mechanism are conducted on the three datasets. The results, shown in Table 6, demonstrate the effectiveness of the attention mechanism. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & & **Proposed Model** & **No MP** & **No Res** & **No Attention** \\ \hline \multirow{4}{*}{IP} & OA & 99.64 \(\pm\) 0.08 & 99.57 \(\pm\) 0.11 & 99.48 \(\pm\) 0.13 & 99.51 \(\pm\) 0.14 \\  & AA & 99.26 \(\pm\) 0.32 & 99.11 \(\pm\) 0.46 & 99.13 \(\pm\) 0.43 & 99.12 \(\pm\) 0.5 \\  & K & 99.59 \(\pm\) 0.09 & 99.47 \(\pm\) 0.13 & 99.40 \(\pm\) 0.14 & 99.45 \(\pm\) 0.15 \\  & OA & 99.97 \(\pm\) 0.02 & 99.95 \(\pm\) 0.02 & 99.95 \(\pm\) 0.02 & 99.94 \(\pm\) 0.07 \\ \multirow{4}{*}{UP} & AA & 99.92 \(\pm\) 0.04 & 99.86 \(\pm\) 0.06 & 99.89 \(\pm\) 0.06 & 99.90 \(\pm\) 0.06 \\  & K & 99.96 \(\pm\) 0.02 & 99.93 \(\pm\) 0.03 & 99.94 \(\pm\) 0.03 & 99.92 \(\pm\) 0.09 \\  & OA & 99.84 \(\pm\) 0.03 & 99.81 \(\pm\) 0.03 & 99.79 \(\pm\) 0.04 & 99.79 \(\pm\) 0.11 \\ \multirow{4}{*}{My Dataset} & AA & 99.70 \(\pm\) 0.05 & 99.65 \(\pm\) 0.06 & 99.56 \(\pm\) 0.13 & 99.63 \(\pm\) 0.08 \\  & K & 99.81 \(\pm\) 0.04 & 99.78 \(\pm\) 0.04 & 99.74 \(\pm\) 0.06 & 99.74 \(\pm\) 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effectiveness of morphological processing (MP), residual connections, and attention mechanism. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & & **17 \(\times\) 17** & **19 \(\times\) 19** & **21 \(\times\) 21** & **23 \(\times\) 23** & **25 \(\times\) 25** \\ \hline \multirow{4}{*}{IP} & OA & 99.41 \(\pm\) 0.19 & 99.54 \(\pm\) 0.1 & 99.64 \(\pm\) 0.08 & 99.59 \(\pm\) 0.13 & 99.57 \(\pm\) 0.16 \\  & AA & 99.07 \(\pm\) 0.53 & 99.16 \(\pm\) 0.36 & 99.26 \(\pm\) 0.32 & 99.13 \(\pm\) 0.33 & 99.16 \(\pm\) 0.32 \\  & K & 99.32 \(\pm\) 0.22 & 99.46 \(\pm\) 0.11 & 99.59 \(\pm\) 0.09 & 99.54 \(\pm\) 0.15 & 99.56 \(\pm\) 0.09 \\  & OA & 99.96 \(\pm\) 0.02 & 99.96 \(\pm\) 0.03 & 99.97 \(\pm\) 0.02 & 99.95 \(\pm\) 0.02 & 99.93 \(\pm\) 0.04 \\  & AA & 99.91 \(\pm\) 0.05 & 99.90 \(\pm\) 0.05 & 99.92 \(\pm\) 0.04 & 99.87 \(\pm\) 0.03 & 99.84 \(\pm\) 0.07 \\  & K & 99.94 \(\pm\) 0.03 & 99.95 \(\pm\) 0.04 & 99.96 \(\pm\) 0.02 & 99.94 \(\pm\) 0.02 & 99.91 \(\pm\) 0.05 \\  & OA & 99.83 \(\pm\) 0.04 & 99.82 \(\pm\) 0.03 & 99.84 \(\pm\) 0.03 & 99.83 \(\pm\) 0.05 & 99.81 \(\pm\) 0.03 \\ \multirow{4}{*}{My Dataset} & AA & 99.64 \(\pm\) 0.08 & 99.67 \(\pm\) 0.07 & 99.70 \(\pm\) 0.05 & 99.70 \(\pm\) 0.06 & 99.66 \(\pm\) 0.05 \\  & K & 99.80 \(\pm\) 0.05 & 99.78 \(\pm\) 0.03 & 99.81 \(\pm\) 0.04 & 99.79 \(\pm\) 0.05 & 99.77 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of different-sized patches on accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & & **25\%** & **30\%** & **35\%** & **40\%** & **45\%** \\ \hline \multirow{4}{*}{IP} & OA & 99.58 \(\pm\) 0.06 & 99.50 \(\pm\) 0.08 & 99.64 \(\pm\) 0.08 & 99.61 \(\pm\) 0.12 & 99.60 \(\pm\) 0.08 \\  & AA & 99.13 \(\pm\) 0.43 & 99.18 \(\pm\) 0.32 & 99.26 \(\pm\) 0.32 & 99.11 \(\pm\) 0.5 & 99.15 \(\pm\) 0.36 \\  & K & 99.52 \(\pm\) 0.07 & 99.43 \(\pm\) 0.07 & 99.59 \(\pm\) 0.09 & 99.56 \(\pm\) 0.14 & 99.55 \(\pm\) 0.08 \\  & OA & 99.84 \(\pm\) 0.11 & 99.88 \(\pm\) 0.11 & 99.97 \(\pm\) 0.02 & 99.95 \(\pm\) 0.02 & 99.97 \(\pm\) 0.02 \\  & AA & 99.71 \(\pm\) 0.23 & 99.81 \(\pm\) 0.15 & 99.92 \(\pm\) 0.04 & 99.90 \(\pm\) 0.06 & 99.92 \(\pm\) 0.05 \\  & K & 99.79 \(\pm\) 0.15 & 99.85 \(\pm\)  
As presented in Tables 7-9, the 3D CNN model outperforms the 2D CNN model in terms of classification accuracy. However, the 3D CNN is computationally expensive and suffers from increased model complexity, leading to a higher risk of overfitting. To balance classification accuracy with computational efficiency, this study employs a multiscale convolutional layer consisting of two 3D convolution layers and a DSC layer.