# Satellite SAR Data-based Sea Ice Classification: An Overview
## 6 Conclusions  
Visual interactive interpretation of satellite imagery to derive sea ice information is the main technique for ice charting in several national ice services. The quality of ice classification depends on the skills and experience of ice experts. Experts with experience of airborne visual ice reconnaissance interpret the data of the optical spectral range quite successfully. Visual interpretation of radar data is quite difficult.  
Many researchers are skeptical about using only SAR data for ice type classifications or thickness retrieval. The sea ice parameters retrieval from SAR images using the only backscatter is considered unreliable, and the additionally provided other sources (sensors) data to improve the results is welcomed. The possibilities of combining high resolution SAR together with optical data are of great interest due to the wide coverage that SAR offers. However, regular monitoring of the sea ice in the high latitude Arctic in a combination of space-borne SAR and visual spectral range data is limited by the polar night when the sea ice monitoring is highly required. In general, many papers demonstrate that single-frequency sea ice classification results show some class confusion compared with the reference results, indicating radar wavelength dependent limitations for ice type separation. Observations from several SAR frequencies would be of interest to get the most information about sea ice parameters to implement ice classification. Combining of all these researchers \"wishlist\" for the accurate routinely mapping of ice in operational regime is restricted by the complexity of near-real-time collocated SAR images acquisition, financial means, computation time and at present is possible only for commercial projects.  
Since HV polarization is good to use to distinguish highly smooth surfaces from rough surfaces, utilizing the co-polarization ratios are recommended as well as additional extracted statistical and textural features. New possibilities open up with CP data since they have more polarization information, and give the opportunities to extract new polarimetric features that will improve the ice classification results.  
The more consistent SAR image classification result is reached when the processing chain in general comprises the following major steps: (1) image pre-processing (calibration, incidence angular correction, noise correction, filtering, etc.); (2) allocation of ice objects in the case of segmentation usage; (3) image features extraction; and (4) classification. Most methods typically based on a combination of backscatter intensity, second order texture statistics, and some ancillary information applying different numerical models for segmentation and/or classification. A huge number of investigations have been proposed to develop sea ice types or ice edge/open water classifications techniques, but closer to real fully- or semi-automatic systems are based on NN or SVM methods.  
Based on this review of the ice cover classification studies using high-resolution SAR images, it seems to be possible to automate main ice types retrieval as well as discriminate ice and OW. The sea ice services use satellite SAR images as the major data source, but the daily operation of sea ice is done manually by ice analysts, especially for providing detailed ice charts. This process is very laborious and subjective, so the development of the robust automated ice mapping algorithms is still a hot topic.  
**Funding:** This research was funded by Russian Foundation of Basic Research (grant 18-05-60124).  
**Conflicts of Interest:** The authors declare no conflict of interest.