## 6 Results and Discussion  
ArcGIS PRO was used for the calculations for all the methods. ArcGIS for Desktop was used to perform the accuracy assessment and data visualization. The calculations of the Kappa index were carried out in Supplementary File S1 to make the calculation easier.  
Figure 6: Area of interest—Skfrán.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Parameters** \\ \hline Manual Identification & Manual vectorization of areas into 2 classes \\ Iso Cluster & Classification into 2 classes; 20 iterations \\ Maximum Likelihood & Train set based on manual selection of each class; 5 samples per class \\ Random Trees & Same train set as Maximum Likelihood; number of trees: 50; tree depth: 30; maximum number of \\ samples per class 1000 \\ SVM & Same train set as Maximum Likelihood; number of samples per class: 500 \\ Deep Learning & Same train set as Maximum Likelihood; U-net (convolutional neural network) pixel classification; \\  & ResNet-34 (convolutional neural network with 34 layers) as Backbone model; 10 epochs \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of methods.  
Figure 7: Area of interest—Baroch.  
The following methods were used: Iso cluster, Maximum Likelihood, random trees, support vector machine (SVM) and deep learning (supported by ArcGIS PRO version 2.6). Both collected images were used for image classification by selected the methods. The methods were set to classify land covers into two classes, only allowing their comparison from the point of view of ice surface differentiation from all other land covers. Thus, the chosen classes were ice and all other covers. Generally, supervised methods provide better results, but it highly depends on the selected training sets. In the case of Skrir, deep learning was the best classifier, with a 92% classification success, see Figure 8. The worst classification method was the Iso cluster, with a 69% classification success, see Figure 9. In the case of Baroch, Maximum Likelihood was the best classifier, with a 93% classification success. The worst classifier was the Iso cluster, with an 88% classification success.  
There was a problem with the classification of the edge of islands in the case of the Baroch image, see Figure 10. The edge consisted of frozen vegetation and pieces of pure ice. In the RGB spectre, the combination looks like pure ice and has almost the same pixel colour as ice. In this case, the worst method had a slightly worse result.  
Table 4 shows the results of all the classification methods. The support vector machine can be considered as the best method on average. The SVM provided the second-best results in both areas, with an accuracy of around 90%.  
Results of all classifications for both areas of interest are visualized, see Figure 11.  
Figure 8: Skrir, the best classification.  
Figure 9: Skrir, the worst classification.  
Figure 11: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Pond Skr\(\ddot{\text{n}}\)** & **Pond Baroch** \\ \hline Manual identification—reference data & 1 & 1 \\ Iso Cluster & 0.6946 & 0.8842 \\ Maximum Likelihood & 0.8464 & 0.9312 \\ Random Trees & 0.8175 & 0.9175 \\ Support Vector Machine & 0.8940 & 0.9225 \\ Deep Learning & 0.9212 & 0.8889 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results—Kappa coefficient.  
Figure 10: Detail of Baroch’s frozen edges.  
The results are in agreement with other authors. Overall, the supervised methods provide better results than the unsupervised methods. The classification accuracy of the supervised methods is around 90%, in line with the article [34]. Remarkably, the deep learning method provided the highest level of accuracy in our study. This result aligns with other authors [37; 40]. The unsupervised method provided worse results than supervised methods, and its accuracy was around 70%. On the other hand, unsupervised methods can provide similar results to supervised methods in specific cases of input data (e.g., homogeneous areas).