## 3 Methodology
### Selection of Classification Metrics  
The deep learning evaluation standard consists of _TP_, _FN_, _FP_, and _TN_ (Table 3).  
T = True and F = False, indicating whether the prediction is correct; P = Positive and N = Negative, indicating whether the prediction result is positive or negative.  
_TP_ = True Positive: the number of positive classes predicted by the actual positive class; _FN_ = False Negative: the number of negative classes predicted by the actual positive class.  
_FP_ = False Positive: the number of positive classes predicted by the actual negative class; _TN_ = True Negative: the number of negative classes predicted by the actual negative class.  
The following formulas (Equations (1) and (2)) help to calculate the expected value through the error matrix:  
\[TP_{expected}=\frac{(TP+FP)\times(TP+FN)}{TP+FP+TN+FN} \tag{1}\]  
\[TN_{expected}=\frac{(TN+FN)\times(FP+TN)}{FP+TP+FN+TN} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Explanation** & **Value** \\ \hline N\_estimators & Maximum number of weak learners (decision trees). & 1000 \\ \hline Criterion & Criteria for evaluating features when dividing decision trees. The options are & \\  & ‘Gini’ for Gini impurities and ‘entropy’ for information gain. & Gini \\ \hline Max\_features & Maximum number of features considered when dividing decision trees. & None \\ \hline Max\_depth & Decision tree maximum depth. & None \\ \hline Min\_samples\_split & Minimum number of samples required for internal node subdivision. & 10 \\ \hline Min\_samples\_leaf & Minimum number of samples for leaf nodes. & 1 \\ \hline Training Threshold Contribution & Determines the contribution of internal weights related to the activation & 0.9 \\ \hline Training Rate & The larger the parameter value, the faster the training. speed, but it also & 0.2 \\ \hline Training Momentum & The function of this parameter is to cause the weight to change in the & 0.9 \\ \hline Training RMS & At this specific value of RMS error, the training should stop. & 0.1 \\ Exit Criteria & The number of hidden layers used. & 1 \\ \hline Number of & The number of iterations used for training. & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RF-CNN model parameter settings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{3}{c}{**Prediction**} \\ \cline{2-4}  & **1** & **0** \\ \hline
**Real** & 1 & _TP_ & _FN_ \\
0 & _FP_ & _TN_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Deep learning evaluation criteria.  
In remote sensing image segmentation, the recall rate, precision rate, and F-measure are commonly used as segmentation performance evaluation indicators [70; 71]. The recall rate represents the ratio of pixels divided into glaciers to actual glacier pixels. A high recall rate indicates better glacier extraction; however, this value cannot discern whether the background is more likely to be mistakenly classified as a glacier. The precision rate represents the ratio of pixels that are correctly segmented into glaciers to all pixels that are segmented into glaciers, which indicates the accuracy of glacier segmentation without considering the background of remote sensing images. The F-measure is a comprehensive performance indicator that strikes a balance between the recall rate and precision rate. A higher F-measure value indicates that the recall rate is higher when the precision rate is higher, which means that the actual segmentation accuracy is higher. The specific experimental performance index calculation formula is shown in Table 4.