# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 3 Theory and Methods
### Sea Ice Classification Method
#### 3.2.3 Sea Ice Classification Based on RF and SVM  
In this study, the sea surface is divided into three categories, including OW, FYI, and MYI. As a powerful ML classifier, RF can deal with both binary and multi-classification problems. The strategy of one-against-one and one-against-all can be chosen to address the multi-classification problem. The RF classifier can be split into several binary classifiers using a one-against-all strategy ([PERSON] et al., 1995) [59]. Although the standard RF algorithm can deal with multi-type problems, the one-against-all binarization to the RF can achieve better accuracy with smaller forest sizes than the standard RF [60]. In addition, the SVM is usually used for binary classification. Thus, the one-against-all binarization strategy is used in this study to classify OW, FYI, and MYI in two steps. In the first stage, the FYI and MYI are regarded as one category (sea ice), whereas the OW is regarded as another category. Then, the classification of sea ice types (FYI and MYI) will be carried out in the second stage using a similar method. The python software package from the Scikit-Learn is adopted in this study [61]. The process flow of sea ice classification is shown in Figure 8.  
The sea ice classification method can be generally categorized into four parts:  
* TDS-1 data preprocessing and features extraction. Firstly, the TDS-1 data with a latitude above \(55^{\circ}\)N and peak SNR above \(-3\) dB is adopted to extract delay waveforms, which are further normalized to extract features. A total of eight features, namely SR, DDMA, RESC, RESI, RESD, REWC, REWI, and REWD, are extracted from the TDS-1 data. Then the TDS-1 features are matched with OSISAF SIT maps based on the data collection date and specular point position through a bilinear interpolation approach. * OW-sea ice classification. In this step, the FYI and MYI are regarded as one category (i.e., sea ice). 30% of samples are randomly selected as training set and the rest 70% of samples are used to test the OW-sea ice classification results. * FYI-MYI classification. The sea ice datasets are applied in this step to classify FYI and MYI. As with the process of OW-sea ice classification, sea ice samples are randomly selected as training and test sets to classify FYI and MYI. * Performance evaluation of sea ice classification. The classification performance is firstly evaluated using the confusion matrix and some evaluation metrics, which are defined in Figure 9 and Table 3, respectively. The row and column represent the actual and predicted types, respectively. As shown in Table 3, _TP_ stands for the number of positive samples that are classified correctly. _FN_ represents the number of positive samples that are classified into a negative type incorrectly. _FP_ is the number of negative samples that are classified into a positive type incorrectly. _TN_ is the number of negative samples that are classified correctly. The evaluation metrics that are used to evaluate the classification performance are defined in Table 3. _Accuracy_ represents the ratio of the correct classification number in all the samples. _Precision_ depicts the precision of predicted results that positive samples are classified correctly. _Recall_ means the completeness of the samples that are classified correctly in all positive samples. _F-value_ is the harmonic mean of classification performance for positive samples, where \(\beta\) is usually  
Figure 8: Flowchart of sea ice classification using machine learning classifiers. Firstly, the TDS-1 data is pre-processed (noise elimination, data filter, and selection, delay waveform extraction, and normalization) to extract features. Then the TDS-1 features are matched with the reference sea ice type (SIT) maps from OSISAF. In the first step, the whole datasets are used to classify open water (OW) and sea ice which includes first-year ice (FYI) and multi-year ice (MYI). In the second step, the sea ice datasets are applied to classify FYI and MYI.