# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 4 Results
For illustration purposes, parts of classifications are shown in Figure 13, which demonstrates the predicted, reference, and their comparison results. Firstly, sea ice and OW are labeled as \(-1\) and \(1\), respectively, in the OW-sea ice classification, whereas FYI and MYI are labeled as \(-1\) and \(1\), respectively, in the FYI-MYI classification. The comparison results between predicted and reference types are computed as follows,  
\[Diff=Ref-Pre, \tag{14}\]  
where _Ref_ represents the value of reference types, _Pre_ is the value of predicted types, _Diff_ is the comparison results. In the OW-sea ice classification, \(-2\) represents the sea ice is misclassified as OW (ice-OW), \(2\) represents the OW is misclassified as sea ice (OW-ice), and \(0\) means the predicted and reference type is consistent. In the FYI-MYI classification, \(-2\) represents the FYI is misidentified as MYI (FYI-MYI), \(2\) represents the MYI is misidentified as FYI (MYI-FYI), and \(0\) means the predicted and reference type is consistent. As shown in Figure 12b, c, most OW and sea ice misclassifications of RF and SVM classifiers appear similarly at the points of OW-sea ice transitions. This can also be seen in Figure 13a,b. The confusion matrix in Figure 11c,d presents that the number of FYI misclassifications is much larger than that of MYI misclassifications. This may be due to the large gap in the number of FYI and MYI samples. The overall accuracy is affected less by the MYI misclassifications since the number of FYI samples is more than 10 times that of MYI samples. As shown in Figure 12e,f, both RF and SVM classifiers have lots of misclassifications, which are widely distributed. In general, the number of misclassifications of SVM is more than that of RF, which can also be seen in Figure 13c,d. In addition, the _kappa_ coefficient of OW-sea ice classification is 0.97 both for RF and SVM, whereas that of FYI-MYI is only 0.39 and 0.23 respectively for RF and SVM. In addition, although SVM can achieve comparable accuracy as RF in OW-sea ice classification, the time consumption of SVM is about nine times that of RF for the same test dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & \multicolumn{2}{c}{**OW-Sea Ice Classification**} & \multicolumn{2}{c}{**FYI-MYI Classification**} \\ \cline{2-5}
**Metrics** & **RF** & **SVM** & **RF** & **SVM** \\ \hline _Accuracy_ (\(\%\)) & 98.83 & 98.60 & 84.82 & 71.71 \\ _Precision_ (\(\%\)) & 98.47 & 98.30 & 98.57 & 98.83 \\ _Recall_ (\(\%\)) & 98.07 & 97.56 & 84.79 & 70.19 \\ _F1-value_ & 0.98 & 0.98 & 0.91 & 0.82 \\ _G-mean_ & 0.99 & 0.98 & 0.85 & 0.79 \\ _Kappa coefficient_ & 0.97 & 0.97 & 0.39 & 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The evaluation metrics of open water (OW)-sea ice and first-year ice (FYI)-multi-year ice (MYI) classification. Figure 12: Classification results of random forest (RF) and support vector machine (SVM) classifiers. (**a**) Reference open water (OW) and sea ice. (**b**) Predicted OW and sea ice of RF. (**c**) Predicted OW and sea ice of SVM. (**d**) Reference first-year ice (FYI) and multi-year ice (MYI). (**e**) Predicted FYI and MYI of RF. (**f**) Predicted FYI and MYI of SVM.