# Coal-Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT
## 3 Experimental Results and Analysis
### Comparison of Different Convolutional Neural Networks  
The recognition accuracies of CBAM-VIT, VIT, AlexNet, and VGG-16 networks under the same number of iterations are shown in Figure 7.  
In addition to testing the CBAM-VIT and VIT network models on the gauge RGB image dataset, traditional CNN models (AlexNet, VGG-16) were also evaluated. Without significant data preprocessing, the classification accuracy of CBAM-VIT and VIT in the test set reached 98.5% and 95.3%, respectively, surpassing other neural network models (as presented in the Figure 7). These results suggest that the self-attention mechanism in Vision Transformer can effectively capture global features between hyperspectral data of coal-gangue. Furthermore, the CBAM-VIT network model designed in this study not only employs the pooling operation in the CBAM module to obtain a global feature representation with fewer parameters and calculations but also adaptively captures recognition-related features. Notably, the recognition accuracy of CBAM-VIT outperforms the three  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Type** & **Data Dimension** & **Quantity** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Channel A \\ \end{tabular} } & Coal & \multirow{2}{*}{\(1\times 148\)} & 1200 \\  & & Gangue & & & 1200 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset description.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline
**Type** & \multicolumn{8}{c}{**Coal**} & \multicolumn{8}{c}{**Gangue**} \\ \hline \hline \multirow{4}{*}{
\begin{tabular}{c} Channel B \\ Channel C \\ \end{tabular} } & **H1** & **H2** & **H3** & **H4** & **H5** & **H6** & **H7** & **H8** & **H9** & **H0** & **H1** & **H2** & **H3** & **H4** & **H5** & **H6** & **H7** & **H8** & **H9** & **H0** \\  & & & & & & & & & & & & & & & & & & & & & \\ Channel C & & & & & & & & & & & & & & & & & & & & & & & \\ RGB image & & & & & & & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dataset visualization.  
Figure 7: Accuracy of different classification networks.  
CNN-based network models under all working conditions. To verify the competitiveness of the proposed CBAM-VIT network model, t-SNE technology was used to visualize the final output distribution of each neural network model in two-dimensional space. The visualization results, illustrated in Figure 8, depict coal in red and gangue in blue.  
To further evaluate the effectiveness of CBAM-VIT, this study thoroughly assesses its recognition accuracy across various operational scenarios. Additionally, the spectral data of coal-gangue are included in the ideal experimental setting. Figure 9 presents the confusion matrix of CBAM-VIT across ten different working conditions. In this matrix, the rows and columns represent the predicted and true labels, respectively. The diagonal cells indicate the accuracy of gangue identification. Notably, the CBAM-VIT network model achieves a classification accuracy exceeding 96% across all working conditions, as evident from the confusion matrix. These results demonstrate the superior performance of the CBAM-VIT network model.