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In this study, a technique for multifractal classification (MC) of synthetic aperture radar (SAR) images of ice-covered sea areas is proposed. This technique is based on the use of SAR image local Hölder exponents (LHEs) and coarse Hölder exponents (CHEs) calculated for sliding windows with different sizes. Hölder exponents are very effective and easily computable image texture descriptors that characterize the degree of local irregularity of image functions. The main steps of the presented SAR image classification technique are following: Sentinel-1 SAR image transformation (application of orbit file, radiometric calibration, speckle filtering, terrain correction and conversion to dB), extracting local and coarse Hölder exponents from HH- and HV-polarized Sentinel-1 SAR images, stacking local and coarse Hölder exponents into high-dimensional feature vectors, classification of the formed feature vectors by some classifier. Experimental testing of the proposed technique for classification of SAR images was carried out on several regions of Sentinel-1b SAR images demonstrating ice-covered areas of the Kara Sea. The first step of the technique was implemented by SNAP toolbox, and the next three steps were implemented using own MATLAB application (https://github.com/UchaevD/GMAToolbox). SAR image classification results were compared with ice charts of U.S. National Ice Center (NIC), which contain weekly information on sea ice concentration and ice thickness. As a result of comparison with NIC ice charts, it has been established that Kara Sea areas with widely-spreading types of floating ice can be successfully separated by MC of Sentinel-1b SAR images, and overall and average classification accuracies are not less than 75%. The results of the study suggest that MC of SAR images of ice-covered sea areas can be used to automate the generation daily ice charts for various ice-covered sea areas in the Arctic and Antarctic.
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