## 4 Results  
We characterize the quality of model training using the recall, loss, precision, accuracy, and F-measure values of the batch, epoch, and epoch_valiation. Taking the same iteration 25 times as an example, it is found that the learning rate of the RF model is too low and the training time is too long. It is necessary to increase the learning rate and retrain the model. The CNN model performance dropped sharply initially, but it soon returned to its original level. In the later stages, the learning rate is too large, failing to fit the results adequately. Therefore, the learning rate should be reduced and the next few rounds of training should be retrained. By adjusting the hyperparameters of the RF-CNN model, optimization is achieved (Figures 8-10). Each sample in a single-label classification task has only one category. If the category is predicted, the classification is correct, and if it is not predicted, it is recorded as a classification error. Therefore, the most intuitive indicator is accuracy, which denotes the accuracy rate. The accuracy rate of the models that we trained (RF, CNN, and RF-CNN) is extremely close to 100% (Figures 8-10), which directly indicates that the constructed model is suitable for the classification of debris-covered glaciers. Furthermore, the accuracy rate of the RF-CNN model is better than that of the other two models, which is in line with our expectations. When we iterate the data 1000 times, the root mean square (RMS) value begins to oscillate around 0.829, which means that the overfitting phenomenon has been greatly alleviated, and the model can obtain good generalization performance. For automatic glacier classification based on remote sensing images, the results usually contain many small spots or holes. To improve the accuracy of image classification, we eliminated or reclassified these small holes. The distribution map of the debris after classification is shown in Figure 11. \begin{table}
\begin{tabular}{c c} \hline
**Measure Name** & **Formula** \\ \hline Recall & \(\text{Recall}=\frac{TP}{TP+FN}\) \\ \hline Precision & \(\text{Precision}=\frac{TP}{TP+FP}\) \\ \hline Accuracy & \(\text{Accuracy}=\frac{TP+TN}{TP+TN+FN+FP}\) \\ \hline F-measure & \(\text{F}-\text{Measure}=2\times\frac{\text{Precision}+\text{Recall}}{ \text{Precision}+\text{Recall}}\) \\ \hline Kappa & \(\text{Kappa}=\frac{(TP+TN)-(TP_{\text{\emph{repret}}+TN_{\text{\emph{appret}}} )}}{(TP+FP+TN+FN)-(TP_{\text{\emph{repret}}}+TN_{\text{\emph{repret}}})}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Performance metrics based on the error matrix. Figure 8: Batch performance index extracted for debris-covered glaciers based on different models (RF: random forest; CNN: convolutional neural network; and RF-CNN: RF training based on CNNs). (**a**) represents batch accuracy of three models; (**b**) represents batch loss of three models; (**c**) represents batch precision of three models; and (**d**) represents batch recall of three models. Figure 9: Epoch performance index extracted for debris-covered glaciers based on different models (RF: random forest; CNN: convolutional neural network; and RF-CNN: RF training based on CNNs). (**a**) represents epoch accuracy of three models; (**b**) represents epoch loss of three models; (**c**) represents epoch precision of three models; and (**d**) represents epoch recall of three models. Figure 11: Distribution map of the debris after classification: (**a**) Eastern Pamir and (**b**) Nyaingentanglha. Figure 10: Epoch_Valiation performance index extracted for debris-covered glaciers based on different models (RF: random forest; CNN: convolutional neural network; and RF-CNN: RF training based on CNNs). (**a**) represents epoch valiation accuracy of three models; (**b**) represents epoch valiation loss of three models; (**c**) represents epoch valiation precision of three models; and (**d**) represents epoch valiation recall of three models. The classification performance of the classifier is evaluated using selected validation samples.