# Satellite SAR Data-based Sea Ice Classification: An Overview
## 5 Discussion
### SAR Data-based Methods for Ice Classification  
Many SAR data-based methods for sea ice classification have been proposed utilizing unsupervised or supervised techniques. Autocorrelation methods [31; 33], combinations of wavelet-based [80; 81; 82], Gabor wavelet techniques [79], MRF [36; 83; 84], clustering approaches [85; 22] have evolved to produce reliable results (see Table 1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sea Ice** & **Input/Image Features** & **Results and Comments** & **Reference** \\ \cline{3-5}
**Algorithm** & **+ Additional Method** & & & \\ \hline \multirow{5}{*}{**Seen Ice**} & \multirow{2}{*}{ERS-1} & ice boundary, ice floes; estimated using a visual & \multirow{2}{*}{[PERSON] et al. (1997) [(80)]} \\  & & & interpretation of 3 scenes 1 [FOOTNOTE:1]Footnote 1: Numerical accuracy not provided. The authors mentioned that no general validation is presented because there was one or several scenes and lack of a representative dataset. 2 CBERS-02B is the China-Brazil Earth Resources Satellite (optical images). 3 PCA, principal component analysis. 4 MASIE, Multisensor Analyzed Sea Ice Extend product, provided by NIC. \\ \cline{2-5}  & \multirow{2}{*}{RS-1/Entropy (texture)} & \multirow{2}{*}{ice boundary; estimated using visual interpretation 1 [FOOTNOTE:1]Footnote 1: Numerical accuracy not provided. The authors mentioned that no general validation is presented because there was one or several scenes and lack of a representative dataset. 2 CBERS-02B is the China-Brazil Earth Resources Satellite (optical images). 3 PCA, principal component analysis. 4 MASIE, Multisensor Analyzed Sea Ice Extend product, provided by NIC. \\ \cline{2-5}  & \multirow{2}{*}{RS-1/texture + MRF} & better detection of smooth and rough F  
Numerous attempts have been made to develop an automatic algorithm for ice/ocean discrimination using SAR data [28; 31; 48; 90; 96]. Based on the early studies in [80; 86], a novel method of automatic detection of the ice edge in dual-polarized RS-2 SAR images was proposed to employ the curvelet transform and active contour method [97]. Comparison of the ice edge with that from manually analyzed SAR images demonstrates the effective detection of the ice edge in SAR images by the proposed method. A semi-automated sea ice classification algorithm based on fuzzy rules is presented in [96] for RS-1 data over the Arctic Ocean and has been evaluated for retrieval of the calm OW, wind-roughened water, and sea ice in low and high concentrations. This semi-automatic fuzzy-logic algorithm was extended for sea ice classification using derived statistical and texture image features [98]. GLCM entropy measure was found to be effective in classifying SAR images. The described methods were used by the DMI. Another cluster of algorithms for the goals of automated SAR data processing to retrieve sea ice parameters are supervised learning classifications. The supervised learning method requires a training dataset with known samples for the algorithm tuning, and hence the SAR pre-processing to investigate, identify and assign labels to the predefined classes via ice expertise (for instance) is needed. One of the popular methods is neural networks, which gain over the more traditional statistical approaches the ability to implement only the actual data without any prior knowledge on the statistical distribution of the data and determination of their informational content [99]. Another supervised machine learning algorithm widely applied to both sea ice types and ice/water classification last years is a support vector machine [100]. The methods of supervised learning also include a Bayesian classification that is widely used as a theoretically reliable image classification. The difference from other classification methods is that the Bayesian approach is based on the known of a priory probability of a class object existence [101; 102]. The combination of these methods and texture features in addition to various segmentation techniques is also successfully used for sea ice type classification.