## 3 Experimental Results  
In this research, we assessed the capabilities of our proposed model on hyperspectral dataset classification tasks and benchmarked it against leading deep learning techniques. The comparative methods encompassed the Multi-scale 3D Deep Convolutional Neural Network (M3D-DCNN), the CNN-based 3D Deep Learning approach (3D-CNN), the Deep Feature Fusion Network (DFFN), RSSAN, the Attention-based Bidirectional Long Short-Term Memory Network (AB-LSTM), the Transformer-based backbone network named SpectralFormer (SF), and SSFTT. Detailed classification results have been presented in Tables 7-12 below. Our research aims to demonstrate the effectiveness and superiority of the proposed model in land over classification tasks through these experiments and to conduct a comprehensive performance comparison analysis. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Class** & **AB-LSTM** & **3D-CNN** & **SPIFORMER** & **SSFFT** & **MQD-DCNN** & **DFFN** & **RSSAN** & **OUR** \\ \hline [PERSON] & 97.9\(\pm\) 0.17 & 97.93 \(\pm\) 0.24 & 97.87 \(\pm\) 0.36 & 98.18 \(\pm\) 0.14 & 97.95 \(\pm\) 0.30 & 99.38 \(\pm\) 0.18 & 98.94 \(\pm\) 0.23 & 99.45 \(\pm\) 0.08 \\ [PERSON] & 95.43 \(\pm\) 0.41 & 93.47 \(\pm\) 1.25 & 94.47 \(\pm\) 1.39 & 95.52 \(\pm\) 0.64 & 94.18 \(\pm\) 0.93 & 99.03 \(\pm\) 0.14 & 97.75 \(\pm\) 0.39 & 99.27 \(\pm\) 0.18 \\ [PERSON] & 94.2\(\pm\) 1.22 & 92.63 \(\pm\) 2.45 & 97.36 \(\pm\) 0.38 & 95.51 \(\pm\) 2.09 & 99.48 \(\pm\) 0.93 & 99.28 \(\pm\) 0.15 & 98.36 \(\pm\) 1.61 & 99.52 \(\pm\) 1.6 \\ Sorghum & 97.56 \(\pm\) 0.81 & 97.08 \(\pm\) 0.35 & 98.04 \(\pm\) 0.76 & 98.31 \(\pm\) 0.65 & 97.18 \(\pm\) 0.39 & 99.00 \(\pm\) 0.45 & 98.46 \(\pm\) 0.81 & 99.66 \(\pm\) 0.13 \\ Water junction & 93.87 \(\pm\) 3.94 & 97.98 \(\pm\) 1.34 & 93.92 \(\pm\) 3.50 & 93.15 \(\pm\) 2.03 & 86.15 \(\pm\) 5.31 & 98.20 \(\pm\) 0.79 & 94.35 \(\pm\) 1.42 & 98.33 \(\pm\) 0.99 \\ Water junction & 71.74 \(\pm\) 4.89 & 96.56 \(\pm\) 1.43 & 93.21 \(\pm\) 2.89 & 92.50 \(\pm\) 1.29 & 86.56 \(\pm\) 3.32 & 99.25 \(\pm\) 1.54 & 98.32 \(\pm\) 1.49 & 99.12 \(\pm\) 0.35 \\ Korea & 94.00 \(\pm\) 0.95 & 95.83 \(\pm\) 0.68 & 96.11 \(\pm\) 0.29 & 97.55 \(\pm\) 1.12 & 98.40 \(\pm\) 0.28 & 94.52 \(\pm\) 1.04 & 98.42 \(\pm\) 1.04 & 98.34 \(\pm\) 1.29 \\  & 93.25 \(\pm\) 0.88 & 99.24 \(\pm\) 0.38 & 96.21 \(\pm\) 0.36 & 98.20 \(\pm\) 0.35 & 98.42 \(\pm\) 0.38 & 99.55 \(\pm\) 0.49 & 99.50 \(\pm\) 0.39 & 99.26 \(\pm\) 0.23 \\  & 99.55 \(\pm\) 0.09 & 96.42 \(\pm\) 0.14 & 98.32 \(\pm\) 0.35 & 98.42 \(\pm\) 0.36 & 99.50 \(\pm\) 0.49 & 99.75 \(\pm\) 0.11 & 99.48 \(\pm\) 0.16 & 99.43 \(\pm\) 0.08 \\  & 99.41 \(\pm\) 0.05 & 99.49 \(\pm\) 0.05 & 99.49 \(\pm\) 0.09 & 99.75 \(\pm\) 0.05 & 99.34 \(\pm\) 0.09 & 99.76 \(\pm\) 0.04 & 99.74 \(\pm\) 0.03 & 99.81 \(\pm\) 0.07 \\  & 99.55 \(\pm\) 0.09 & 99.44 \(\pm\) 0.14 & 98.96 \(\pm\) 0.26 & 99.43 \(\pm\) 0.16 & 98.41 \(\pm\) 0.17 & 99.41 \(\pm\) 0.16 & 99.43 \(\pm\) 0.08 & 99.57 \(\pm\) 0.06 \\  & 99.40 \(\pm\) 0.08 & 99.41 \(\pm\) 0.05 & 99.49 \(\pm\) 0.09 & 99.75 \(\pm\) 0.05 & 99.34 \(\pm\) 0.09 & 99.76 \(\pm\) 0.04 & 99.74 \(\pm\) 0.03 & 99.81 \(\pm\) 0.07 \\  & 22481 t/s & 207.99 \(\pm\)/s & 41.30 \(\pm\) 0.85 & 36.03 \(\pm\) 1.75 & 129.31 \(\pm\) 1.78 & 198.97 \(\pm\) 1.26 & 298.83 \(\pm\) 1.57 & 157.94 \(\pm\)/s \\ \hline \hline \end{tabular}
\end{table} 
Table 10: WHU-Hi-LongKot classification results. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Class** & **AB-LSTM** & **3D-CNN** & **SPIFORMER** & **SSFFT** & **MQD-DCNN** & **DFFN** & **RSSAN** & **OUR** \\ \hline Sternberry & 97.79 \(\pm\) 0.17 & 97.93 \(\pm\) 0.24 & 97.87 \(\pm\) 0.36 & 98.18 \(\pm\) 0.14 & 97.95 \(\pm\) 0.30 & 99.38 \(\pm\) 0.18 & 98.94 \(\pm\) 0.23 & 99.45 \(\pm\) 0.08 \\ Corppe & 95.43 \(\pm\) 0.41 & 93.47 \(\pm\) 1.25 & 94.47 \(\pm\) 1.39 & 95.52 \(\pm\) 0.61 & 94.18 \(\pm\) 0.93  
Figure 4: Classification map of the Pavia University dataset.