## 5 Discussion
### Feature Analysis  
The correlation coefficient method assigns weights based on the repetitiveness of information between characteristic variables. In categorical data, there is a certain correlation between the selected feature variables. The correlation coefficient method not only avoids allocating excess weights to certain aspects of the data due to data duplication but also objectively reflects the importance of the feature variables [79]. Although the values of correlation coefficients obtained after model training differ, they all exhibit a consistent trend, as shown in Figures 14-16. It is important to note that positive and negative values are equally important in the correlation coefficient graph. Among all the feature values, spectral features and texture features play a decisive role in the classification of debris-covered glaciers, because these two features are dependent on the spectral value of the optical image. Next, in terms of importance, are the three index characteristics (NDSI, NDVI, and NDWI) and LST, and the terrain features derived from the DEMs also had high correlation coefficients. The correlation between terrain features is not very significant. The glacier movement velocity is also affected by topographical factors, especially in the Eastern Pamir area, where the terrain is undulating [11], but its effect is also not significant.  
It is worth noting that, although different studies on glacier mapping currently use different feature variables, these findings contribute in several ways to our understanding of detection at glacier boundaries and provide a basis for more in-depth research.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Glacier Name**} & \multicolumn{6}{c}{**Existing Methods and Inventories (km2)**} \\ \cline{2-7}  & **RGI 6.0** & **CGI 2** & **WCGI 2018** & **STD.** & **Mean** & **RF-CNN** \\ \hline G075254E38623N & 115.162 & 115.162 & 114.833 & 0.155 & 115.052 & 115.588 \\ G075133E38690N & 11.557 & 11.557 & 11.481 & 0.036 & 11.532 & 11.699 \\ G075146E38607N & 17.207 & 17.207 & 17.420 & 0.100 & 17.278 & 17.904 \\ G075262E38523N & 10.365 & 10.365 & 10.599 & 0.110 & 10.443 & 10.846 \\ G075400E38636N & 9.934 & 9.934 & 9.853 & 0.038 & 9.907 & 9.934 \\ G075339E38560N & 86.631 & 86.631 & 83.998 & 1.241 & 85.753 & 84.859 \\ G075304E38449N & 22.950 & 22.950 & 22.914 & 0.017 & 22.938 & 23.192 \\ G075321E38480N & 26.468 & 26.468 & 26.468 & 0 & 26.468 & 26.469 \\ G075457E38631N & 13.901 & 13.901 & 13.882 & 0.009 & 13.895 & 13.930 \\ G075486E38594N & 9.346 & 9.346 & 9.464 & 0.056 & 9.385 & 9.519 \\ G090600E30388N & 27.354 & 27.354 & 27.298 & 0.026 & 27.335 & 27.417 \\ G090618E30355N & 7.133 & 7.133 & 7.105 & 0.013 & 7.124 & 7.229 \\ G090071E29968N & 4.762 & 4.762 & 4.668 & 0.044 & 4.731 & 4.672 \\ G090039E29949N & 3.048 & 3.048 & 2.943 & 0.049 & 3.013 & 3.051 \\ G090040E29912N & 5.444 & 5.444 & 5.438 & 0.003 & 5.442 & 5.717 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of the debris-covered glacier area derived from the automatic extraction algorithms and from other inventories.  
Figure 14: Analysis of the correlation of variables in the RF model for the classification of debris-covered glaciers. * (\(p\leq 0.05\)) denotes a significant correlation.  
Figure 15: Analysis of the correlation of variables in the CNN model for the classification of debris-covered glaciers. * (\(p\leq 0.05\)) denotes a significant correlation.