# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 4 Experiments
### Experimental Results  
Table 1 provides a detailed list of OA, AA, and Kappa values obtained using different band selection algorithms on different datasets. During the classification stage, the stratified sampling approach helps construct training and testing sets. To form the training set, 70% of the samples from each class for each frequency band are randomly selected. This ensures that the training data are representative of the entire dataset. The remaining 30% of the samples are allocated to the testing set, which is used to evaluate the model's performance on unseen data. The results show that CALM showed a certain degree of improvement after introducing the regularization term compared to S\({}^{4}\)P and GRSC in all three evaluation metrics. Compared with ASPS_MN, introducing superpixel segmentation technology makes the band selection results more effective. Under all classifiers and conditions, the OA value of CALM is generally high, with values generally approaching or exceeding 0.9. As shown in Table 1, the new method, CALM, consistently outperforms other methods in terms of different metrics, i.e., OA, AA, Kappa. Taking TeaFarm datasets as an example, the OA of CALM with the SVM classifier increases by 6.88%, 1.86%, 1.71%, 5.39%, and 2.09%; the value of AA increases by 11.53%, 11.41%, 11.52%, 17.81%, and 12.35%; the value of Kappa increases by 25.08%, 2.68%, 2.48%, 7.83%, and 3.02%. As Figures 6-11 demonstrates the visualization result graph under different classification methods. Taking the TeaFarm dataset as an example, from Figures 6 and 7, the areas classified as bamboo forest (red area) and grassland (orange area) are less affected by other categories and exhibit higher color category purity in the classification map. In the tea tree area, the distribution range of tea trees can be more accurately identified, reducing the occurrence of misclassification and missed classification. Compared with other methods, the CALM algorithm has higher classification accuracy in complex regions and significantly reduces speckle noise. The classification results indicate the effectiveness of the CALM method, which is more suitable for subsequent hyperspectral data classification. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & **Classifier** & **Metrics** & **ASPS\_MN** & **S\({}^{\textbf{t}}\)P** & **GRSC** & **RLFFC** & **SEASP** & **CALM** \\ \hline \multirow{6}{*}{TeaFarm} & \multirow{6}{*}{KNN} & OA & 0.9012 & 0.9417 & 0.9508 & 0.9437 & 0.9445 & **0.9522** \\  & & AA & 0.7747 & 0.8576 & 0.8638 & 0.8633 & 0.8647 & **0.8902** \\  & & Kappa & 0.8932 & 0.9152 & 0.9140 & 0.9180 & 0.9194 & **0.9302** \\  & & OA & 0.8802 & 0.9304 & 0.9319 & 0.8951 & 0.9281 & **0.9490** \\  & & AM & 0.7479 & 0.7491 & 0.7480 & 0.6851 & 0.7397 & **0.8632** \\  & & Kappa & 0.6747 & 0.8987 & 0.9007 & 0.8472 & 0.8953 & **0.9255** \\  & & OA & 0.8282 & 0.9150 & 0.9242 & 0.9074 & 0.9201 & **0.9260** \\  & & AN & 0.8830 & 0.9565 & 0.9629 & 0.9537 & 0.9610 & **0.9637** \\ \multirow{2}{*}{Salinas} & \multirow{6}{*}{SVM} & Kappa & 0.8172 & 0.9075 & 0.9174 & 0.8994 & 0.9129 & **0.9193** \\  & & OA & 0.8318 & 0.9318 & 0.9458 & 0.8908 & **0.9478** & 0.9469 \\  & & AA & 0.8849 & 0.9673 & 0.9738 & 0.9320 & 0.9721 & **0.9757** \\  & & Kappa & 0.8210 & 0.9255 & 0.9259 & 0.8812 & **0.9418** & 0.9417 \\  & & OA & 0.7184 & 0.7678 & **0.8644** & 0.7446 & 0.7653 & 0.8363 \\  & & AA & 0.7324 & 0.7678 & 0.8000 & 0.6824 & 0.7150 & **0.8285** \\  & & Kappa & 0.6885 & 0.7479 & 0.7807 & 0.7234 & 0.7449 & **0.8285** \\ \multirow{2}{*}{Indian Pines} & \multirow{2}{*}{SVM} & OA & 0.7552 & 0.8616 & 0.8971 & 0.8009 & 0.8799 & **0.9016** \\  & & AA & 0.7284 & 0.8626 & 0.8976 & 0.7412 & 0.8852 & **0.8989** \\ \multirow{2}{*}{Indian Pines} & \multirow{2}{*}{SVM} & Kappa & 0.7352 & 0.8477 & 0.8857 & 0.7821 & 0.8671 & **0.8907** \\ \hline \hline \end{tabular} NOTE: The best results on the quality index are labeled in bold.  
\end{table} 
Table 1: Classification results of different methods with three metrics on different datasets.