# Satellite SAR Data-based Sea Ice Classification: An Overview
## 5 Discussion
### SAR Data-based Methods for Ice Classification
Several early studies present the successful application of NN in the development of algorithms for sea ice classification from satellite images (e.g., [103; 104; 105]). [PERSON] [106] presented the pulse-coupled NN for ice edge detection, segmentation and ice classification in RADARSAT SAR images of the Baltic Sea. The results show the correct classification of new, level FYI, deformed and fast ice, although thick fast ice was classified in some cases as thin level ice. This approach is widely investigated and extended, has several iterations according to the data and some features, and now modified techniques are designed and applied in the routine process of concentration and thickness ice charts drawing in FIS over Baltic Sea for all seasons [34; 35]. [PERSON] et al. [91] compared the NN classification with the other supervised learning algorithm based on the linear discriminant analysis (LDA), presented in [107], for ice classification. A multi-layer feed-forward NN was applied to recognize six sea ice types in RADARSAT and ERS SAR images of the Kara Sea. The results of automated classification (see Table 1) are significantly improved by applying the multisensors approach (up to 91.2% and 90.1% for NN and LDA methods, respectively). A NN with texture features based on the GLCM was used for classification of ENVISAT ASAR images [89], where the approach of two consistent application of differently trained NN was proposed to improve sea ice types and OW discriminating using one polarization. The same approach is shown in [93] for the sea ice type classification of RS-2 dual-polarization images using the second SVM in combination with texture features. [PERSON] et al. [93] reasoned that ice concentration should be exploited during the first initial SVM classification and applied as one of the input parameters for a second SVM iteration. They also applied the ML classification to compare its results with the SVM classification. In conclusion, the SVM was found to be more reliable for sea ice classification than ML. SVM is successfully utilized in MAp-Guided Sea Ice Classification system [40] (see Table 1). This system has been under development by MAGIC research group for many years, investigating, applying and improving the combination of various texture extraction, image segmentation and classification approaches [36; 37; 40; 85]. A similar approach applying SVM with backscatter and texture features is presented in [95; 108] (see Table 1) for classification of S1 data. The geostatistical approach to automated classification of sea ice from SAR data proposed in [109], where various function values are derived from sub-images in several directions. The classification is then based on feature vectors of parameters (various parameters) calculated from the various functions to summarize spatial sea ice properties. The result yields a segmentation of the study area into homogeneous regions of different sea ice types. It is summarized that a combination of supervised classification (ML) and various parameter mapping yields best results, correctly identifying several sea ice _provinces_. A province can contain several sea ice types that have experienced the same morphogenetic, kinematic and dynamic processes. Therefore, the geostatistical-statistical classification may be applied to detect processes describing physical characteristics dynamics and deformation states of sea ice.