## 4 Conclusions  
In this paper, we propose a hyperspectral image classification method that combines convolutional neural networks (CNNs), depth-separable convolution (DSC), multiscale convolution, residual connections, attention mechanisms, and max pooling (MP). By employing multiscale feature extraction and splicing hyperspectral data after PCA dimensionality \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Time** & **IP** & **PU** & **My Dataset** \\ \hline SVM & Train(s) & 2.11 & 5.32 & 3.94 \\  & Test(s) & 1.03 & 2.31 & 1.84 \\
2D CNN & Train(m) & 1.12 & 1.31 & 1.53 \\  & Test(s) & 0.9 & 1.52 & 1.61 \\
3D CNN & Train(m) & 3.21 & 8.17 & 16.63 \\  & Test(s) & 8.51 & 13.04 & 30.26 \\ SSRN & Train(m) & 4.73 & 6.93 & 14.11 \\  & Test(s) & 5.89 & 11.36 & 25.13 \\ HybridSN & Train(m) & 2.98 & 3.65 & 7.43 \\  & Test(s) & 1.98 & 2.06 & 4.92 \\ My Net & Train(m) & 2.64 & 3.99 & 6.56 \\  & Test(s) & 1.23 & 1.63 & 2.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Model training and testing times. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **SVM** & **2D CNN** & **3D CNN** & **SSRN** & **HybridSN** & **Proposed Model** \\ \hline
1 & 95.31 & 99.77 & 98.04 & 99.85 & 100 & 99.98 \\
2 & 96.64 & 100 & 97.03 & 99.98 & 99.95 & 99.99 \\
3 & 83.5 & 99.75 & 95.08 & 99.93 & 100 & 100 \\
4 & 95.36 & 100 & 99.67 & 99.91 & 99.91 & 99.91 \\
5 & 99.43 & 51.93 & 100 & 100 & 100 \\
6 & 89.69 & 99.80 & 99.63 & 100 & 100 & 100 \\
7 & 88.23 & 99.25 & 96.72 & 100 & 99.68 & 100 \\
8 & 87.39 & 95.93 & 92.22 & 98.81 & 99.92 & 99.92 \\
9 & 99.81 & 100 & 99.65 & 99.55 & 99.55 & 99.85 \\ OA & 95.01 \(\pm\) 0.21 & 96.63 \(\pm\) 0.21 & 97.27 \(\pm\) 0.13 & 99.85 \(\pm\) 0.09 & 99.92 \(\pm\) 0.04 & 99.97 \(\pm\) 0.02 \\ AA & 93.41 \(\pm\) 0.53 & 95.56 \(\pm\) 0.36 & 96.22 \(\pm\) 1.23 & 99.76 \(\pm\) 0.33 & 99.80 \(\pm\) 0.14 & 99.92 \(\pm\) 0.04 \\ \(\kappa\) & 92.86 \(\pm\) 0.39 & 95.40 \(\pm\) 0.51 & 96.36 \(\pm\) 0.21 & 99.79 \(\pm\) 0.15 & 99.90 \(\pm\) 0.05 & 99.96 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification accuracy results on PU dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **SVM** & **2D CNN** & **3D CNN** & **SSRN** & **HybridSN** & **Proposed Model** \\ \hline
1 & 79.38 & 100 & 95.48 & 100 & 100 & 100 \\
2 & 97.41 & 68.23 & 99.26 & 97.65 & 98.54 & 99.37 \\
3 & 95.43 & 99.89 & 97.38 & 99.59 & 99.42 & 99.45 \\
4 & 99.10 & 100 & 99.84 & 99.79 & 100 & 100 \\
5 & 95.59 & 100 & 99.92 & 100 & 99.98 & 100 \\
6 & 95.36 & 100 & 99.24 & 100 & 100 & 100 \\
7 & 99.5 & 100 & 100 & 100 & 100 & 100 \\ OA & 95.43 \(\pm\) 0.55 & 96.92 \(\pm\) 0.24 & 98.79 \(\pm\) 0.21 & 99.78 \(\pm\) 0.13 & 99.81 \(\pm\) 0.04 & 99.84 \(\pm\) 0.03 \\ AA & 93.21 \(\pm\) 2.63 & 97.52 \(\pm\) 0.45 & 98.50 \(\pm\) 0.53 & 99.64 \(\pm\) 0.28 & 99.67 \(\pm\) 0.1 & 99.70 \(\pm\) 0.05 \\ \(\kappa\) & 95.29 \(\pm\) 0.63 & 97.52 \(\pm\) 0.24 & 98.52 \(\pm\) 0.43 & 99.72 \(\pm\) 0.25 & 99.76 \(\pm\) 0.07 & 99.81 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification precision results on My Dataset. reduction, the model is capable of simultaneously extracting spectral and spatial features, thereby enhancing classification accuracy. The introduced attention mechanism effectively models the interdependencies between different channels, further improving network performance. The use of residual connections and dropout layers effectively mitigates the issues of gradient vanishing and overfitting, thereby enhancing the model's generalization ability. The experimental results on three real datasets demonstrate that the proposed method achieves excellent classification accuracy. However, the model exhibits high computational complexity, and there remains a risk of overfitting in certain cases, particularly with small sample datasets. Future research could focus on improving the model's performance and applicability by simplifying the network architecture, optimizing computational efficiency, and expanding to additional datasets.