# Satellite SAR Data-based Sea Ice Classification: An Overview
## 4 SAR Data-based Ice Classification
The general conclusion was that the performance of the algorithm reveals a considerable dependence on the ice cover characteristics in certain regions but does an excellent job of mapping sea ice in regions to which it is optimally adapted [22; 23]. In 1998, Multi-Year Ice Mapping System (MIMS) at the University of Colorado was developed to quickly identify of the high-latitude old ice using uncalibrated SAR images from RADARSAT. A local dynamic thresholding algorithm using the Fisher criterion automatically maps and contours multiyear (old) ice. MIMS was tested and evaluated at the Naval Research Laboratory at the Stennis Space Center (NRLSSC) in Mississippi [24; 25]. In addition, in 1998, a beta version of the fully automated system was designed and delivered to NIC and NRLSSC. The Advanced Reasoning using Knowledge for Typing of Sea ice (ARKTOS) system, evolved over more than 10 years, is a SAR-based sea ice classification system incorporated the knowledge-based rules of regional ice features to interpret RADARSAT SAR images [26]. After pre-processing of the input SAR image, ARKTOS applies a watershed region growing segmentation technique to identify intensity homogeneous regions (segments) in the image. A set of 25 attributes, which describe the properties of each segment, such as average intensity, area, shape and other spatial characteristics, and the simple texture-based statistics, is computed. Each segment is then classified according to a set of rules derived from the knowledge and ice experts experience using Dempster-Shafer belief system by looking for satisfied conditions. The algorithm allows distinguishing four classes: OW, new ice, FYI and MYI. The main advantage of ARKTOS is it is a feature-based approach, while most knowledge-based systems are pixel-based. Human experts do not analyze the images at the pixel level; instead, they look at regions and features and reason about them. The ARKTOS system can classify SAR sea ice data automatically in an operational regime by simulating logical conclusions used by ice experts in the process of interpretation [27; 28]. Similar to the GPS system for the processing of geophysical data products, the RADARSAT Geophysical Processor System (RGPS) was developed at the Jet Propulsion Laboratory and installed in the ASF since 1999. The RGPS generated many products built on each other. The elaborate operational system classifies Scanning Synthetic Aperture Radar (ScanSAR) mode images of the RS-1 SAR in winter into five classes combining the ice backscatter values with ice motion and an empirical relation between accumulated freezing-degree days and ice thickness. The system identifies MYI, FYI rough, FYI smooth, and a class that combines new ice and smooth OW [29; 30]. Finnish Meteorological Institute (FMI) has developed several generations of algorithms for the estimation of sea ice concentration, and now offers precise real-time information on ice cover and concentration for the Baltic Sea. The Baltic Sea ice and OW discrimination with 90% accuracy based on thresholding of segment-wise local autocorrelation using SAR images was presented by [PERSON] et al. [31]. Then, [PERSON] (2010) [32] showed that several classes of sea ice can be distinguished by selecting the boundaries in the RS-1,2 and ENVISAT SAR images using the Canny edge detection. These methods can well distinguish OW areas, as well as different ice types and areas with certain types of ice characteristics (for example, cracks or ridges). A comparison of the ice-water classification results of these two algorithms application was presented by tests with RS-2 and ENVISAT SAR data that showed the classification accuracy over 89.4% compared with ice maps of the Baltic Sea manually produced by the FIS. For training data, the classification result of both methods has more than 92% accuracy [32; 33]. A fully automated algorithm using dual-polarized ScanSAR wide mode RS-2 data for sea ice concentration retrieval [34] has been applied by FIS in an operational test mode since 2014.