# Satellite SAR Data-based Sea Ice Classification: An Overview
## 4 SAR Data-based Ice Classification  
SARs emit the microwave radiation toward the Earth's surface and receive the reflected echo by the same antenna recording the amplitude and phase. Consequently, a SAR image represents the intensity of radar reflections from the surface, which is measured by the value of the backscattering coefficient (\(\sigma^{\circ}\)). The information derived from SAR image depends on the radar frequency, polarization, and incidence angles, as well as the backscattering intensity of the given ice type, is influenced by the physical and dielectric properties of ice [5; 15; 16]. Ice thickness cannot be directly derived from SAR images but can be only roughly estimated from the ice stage of development (ice types). The automatic interpretation of SAR images is very challenging since SAR image interpretation is complicated by the ambiguity of sea ice backscatter. In addition, the need to quickly and routinely process of many delivered SAR images may require the development of the automated techniques. Therefore,the creation of robust automated SAR imagery processing systems has been and remains a very urgent task for researchers. The most common approach to detect sea ice types is the use of the SAR image characteristic--tone or brightness--determined by the sea ice backscatter. The ability to detect different ice stages of development is very dependent on the geographic region and season [8; 17; 18]. The University of Kansas began to study the use of expert systems in sea ice classification from SAR since 1990 resulting in various techniques to measure and identify sea ice features in SAR imagery. The combination of the dynamic local thresholding and an expert system with human geophysical knowledge were investigated for ice-water segmentation and ice type classification from ERS-1 SAR data. The technique uses multiple thresholdings (selected by the dynamic thresholding method) and correlation to extract intermediate result images, which are then morphologically cleaned and recombined through a structural growing technique, resulting in distinctions among three ice thickness classes: 0-30 cm, 30-200 cm, and \(>\)200 cm. The obtained ice classes and their characteristics, which represent a fullness of features in terms of shape, are fed to the established expert system. The proper labeling for the ice thickness classes in the final result image are achieved using geophysical classification knowledge (ice growth and behavior (geometry), geographical, and historical) to supplement the original classification. The proposed dynamic thresholding technique offers good separation of ice classes. Inherent problems caused by the ambiguity of the SAR images ice intensity under varying environmental conditions, as well as windy OW, are remedied to a large extent by the expert systems analysis [19; 20]. In 1992, the Geophysical Processor System (GPS) was developed at the Alaska Satellite Facility (ASF) Synthetic Aperture Radar Data Center, established at the University of Alaska, which was designed to regularly automatic sea ice classification from SAR [21]. The automated classification system was developed for single polarized SAR images from ERS-1 and jointly employed an ISODATA cluster analysis and the Look-Up Tables (LUTs) contain the expected backscatter coefficients of various ice types for a specific geographical region and season [22; 23]. The air temperature fields received from the National Meteorological Center are used to select the appropriate LUTs to be used for classifying sea ice types in the SAR imagery. The use in the classification algorithm of only the backscatter values limited the number of distinguished sea ice types to four, and in the summer period to 2-3. The method of LUTs utilization allows distinguishing four ice types in winter: multiyear ice (MYI), rough first-year ice (FYI), smooth FYI, and a class that combines new ice and smooth OW. The most reliable results of GPS ice types classification were obtained for the estimation of sea ice in the central Arctic.