## 3 Experiments and Discussion  
### Dataset Description  
In this paper, three hyperspectral image datasets [31] (Indian Pines [IP], Pavia University [PU], and the self-made dataset \"My Dataset\") are used to validate the proposed method. Figures 2-4 show diagrams of the true categories of the hyperspectral images. IP: This dataset includes 145 \(\times\) 145 pixels with 200 spectral bands with a spatial resolution of approximately 20 m at wavelengths of 0.5 to 2.5 \(\upmu\)m. The image contains 16 different land-cover categories. PU: This dataset includes 610 \(\times\) 340 pixels with 200 spectral bands with a spatial resolution of approximately 1.3 m at wavelengths of 0.43 to 0.86 \(\upmu\)m. The image contains nine different land-cover categories. My Dataset: This dataset includes 182 \(\times\) 217 pixels with 135 spectral bands at wavelengths of 0.3 to 0.9 \(\upmu\)m. The image contains seven different land-cover categories. Figure 2: IP dataset: (**a**) false-color image, (**b**) ground-truth image, and (**c**) class names. \begin{table}
\begin{tabular}{c c c c c} \hline
**Layer (Type)** & **Kernel Size** & **Stride** & **Padding** & **Output Shape** \\ \hline \hline \multicolumn{1}{c}{Input layer} & & & & Input: [(21,21,20,1)] \\ Conv3D\_1 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_1: (19,19,18,8) \\ Conv3D\_2 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_2: (17,17,16,16) \\ Separable\_Conv3D\_1\_1 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_3\_1\_1: (15,15,14,32) \\ Conv3D\_3\_1\_2 & (1,1,1) & (1,1,1) & (0,0,0) & Out\_3\_1\_2: (15,15,14,16) \\ Separable\_Conv3D\_3\_2\_1 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_3\_2\_1: (15,15,14,32) \\ Conv3D\_3\_2\_2 & (1,1,1) & (1,1,1) & (0,0,0) & Out\_3\_2\_2: (15,15,14,32) \\ Separable\_Conv3D\_3\_2\_3 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_3\_2\_2: (15,15,14,32) \\ Conv3D\_3\_2\_4 & (1,1,1) & (1,1,1) & (0,0,0) & Out\_3\_2\_2: (15,15,14,32) \\ Concatenate 1 (Out\_3\_1\_2, Out\_3\_4) & & & Out\_C\_1: (15,15,14,32) \\ Attention1 & & & Out\_SE1: (15,15,14,32) \\ Residual Connection\_1 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_R\_1: (15,15,14,32) \\ Add (Out\_C1, Out\_R1) & & & Out\_A1: (15,15,14,32) \\ Reshape (Out\_A1) & & & Out\_Re: (15,15,448) \\ Separable\_Conv2D\_4\_1\_1 & (3,3) & (1,1) & (0,0) & Out\_4\_1\_1: (13,13,64) \\ Conv2D\_4\_1\_2 & (1,1) & (1,1) & (0,0) & Out\_4\_1\_1: (13,13,64) \\ Separable\_Conv2D\_4\_2\_3 & (3,3) & (1,1) & (0,0) & Out\_4\_2\_2: (13,13,64) \\ Conv2D\_4\_2\_2 & (1,1) & (1,1) & (0,0) & Out\_4\_2\_2: (13,13,64) \\ Separable\_Conv2D\_4\_2\_3 & (3,3) & (1,1) & (0,0) & Out\_4\_2\_3: (11,11,64) \\ Conv2D\_4\_2\_4 & (1,1) & (1,1) & (1,1) & Out\_4\_2\_4: (13,13,32) \\ Concatenate\_2 (Out\_4\_1\_2, Out\_4\_2) & & & Out\_C\_2: (13,13,64) \\ Attention2 & & & Out\_SE2: (13,13,64) \\ Residual Connection\_2 & (3,3) & (1,1) & (0,0) & Out\_R\_2: (13,13,64) \\ Conv2D\_4\_2\_3 & (1,1) & (1,1) & (1,1) & Out\_4\_2\_4: (13,13,32) \\ Concatenate\_2 (Out\_4\_1\_2, Out\_4\_4) & & & Out\_C\_2: (13,13,64) \\ Attention2 & & & Out\_SE2: (13,13,64) \\ Residual Connection\_2 & (3,3) & (1,1) & (0,0) & Out\_R\_2: (13,13,64) \\ Conv2D\_4\_2\_3 & (1,1) & (1,1) & (0,0) & Out\_R\_2: (13,13,64) \\ Residual Connection\_2 & (3,3) & (1,1) & (0,0) & Out\_R\_2: (13,13,64) \\ Add (Out\_C\_2, Out\_R\_2) & & & Out\_A\_2\_2: (13,13,64) \\ Flatten & & & Out\_F: (10,816) \\ Linear\_2 & & & Out\_L\_2: (128) \\ Dropout\_2 & & & Out\_D\_2: (128) \\ Linear\_3 & & & Out\_L\_3: (7) \\ Total Parameters: 3,269,495 & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Framework structure of My Dataset.