# Satellite SAR Data-based Sea Ice Classification: An Overview
## 5 Discussion
### Ice Classification and SAR Technical Abilities
The advantages of the full polarimetric data for sea ice mapping is limited by the narrow image swath in the purpose of operational sea ice classification and can be useful to monitor small local areas [45; 58; 59]. The upcoming SAR of Canadian satellites RADARSAT Constellation Mission (RCM) in addition to polarimetric modes will provide data of new polarization configuration --hybrid polarization, also known as compact polarization (CP). Several studies assessing the possibility for discrimination of ice types and OW using SAR data obtained in the CP mode have shown promisingresults [60; 61; 62]. The proposed CP SAR data will have a swath width up to 500 km and could be a good choice for operational sea ice observation using SAR [63]. Moreover, to improve the SAR data classification, more complex datasets can be applied. In addition to using the multichannel, fully polarimetric images data can be supplemented by fusion between data acquired by different SAR sensors or between SAR data and multispectral images [64; 65; 66; 67; 68; 53; 64; 69]. Another focus, when images acquired by different sensors are combined, is the availability of imagery with different resolutions that of advantage for sea ice classification [70]. Other potential ability to streamline the ice mapping services is to enhance the satellite data processing techniques. Various approaches have been developed to automate the process of sea ice type classification as well as the location of the ice edge and OW in SAR imagery during the past years. Since several winter sea ice stages of development (types or age) and OW can have the same tonal properties, which represent backscatter values [4; 5; 23], the backscatter at a single pixel is insufficient to distinguish between ice categories. A single-polarization ERS-1 SAR image intensity-based classification with LUTs (algorithm proposed by ASF) have been used to identify MYI, smooth and rough FYI, and new ice/open water in the Beaufort and Chukchi Seas [71]. Additional information is required incorporating the spatial context information (e.g., texture) present in SAR images to analyze objects constructed of similar neighboring pixels. The image analysis method that groups adjacent pixels into significant areas and tracks these areas as objects according to their shape, texture, and spectral information is called an object-oriented classification. This object-based classification allows extracting information about image texture within the group of pixels, which is not available in single pixel-based classifications, making the sea ice parameters delineation more accurate. An important advantage of the object-oriented approach is the independence on the speckle noise of SAR images. Since the texture is manifested due to variations of the measured intensity occurring in scales much larger than the pixel size, the visually considered SAR image structures (floes, ridges, cracks, rafting ice, leads, etc.) may reveal homogeneous or inhomogeneous areas and can be characterized by different textural parameters [5]. The texture feature extraction is a very substantial stage since it affects the quality of the subsequent classification. Many texture features exist across various scales, and it is very important to measure the texture reasonably and effectively. The most widely used techniques to extract the texture information are statistical methods, e.g. wavelet transform, Markov random field (MRF) and others. The most common statistical method is based on a gray level co-occurrence matrix (GLCM). The texture features extraction via GLCM was presented and explored [72; 73; 74], and qualitative evidence of its usefulness for sea ice classification has been found as was one of the key steps for image processing improvement [75]. [PERSON] et al. [75] examined the use of texture features for sea ice classification and recommended incorporating some additional texture features in future work.