Ice Cover Delineation Over Devon Iceland Using Sentinel Polarimetric SAR and Optical Data

• Published in: 2023 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea)
• Main Authors: Iqbal, M. Amjed, Anghel, Andrei, Datcu, Mihai
• Format: Conference Object
• Language: unknown
• Published: 2023
• Subjects: EO Data Science; Devon Island; Iceland
• Online Access: https://elib.dlr.de/201607/; https://ieee-ims.org/event/2023-ieee-international-workshop-metrology-sea-learning-measure-sea-health-parameters

The accurate extraction of ice cover is important on a global scale. Owing to the weather-independent imagery capabilities, Sentinel-1 (S1) is a reliable source for ice cover monitoring using SAR images. Therefore, this study focused on an unsupervised method for extracting ice cover by exploiting dual-pol S1 SAR data over Devon Island, which is surrounded by the ocean. We adopted a constant false alarm rate (CFAR) detector for ice cover detection by examining the empirical distribution of a given matrix over an ice region followed by a statistical Burr distribution to derive the CFAR threshold value. To detect ice cover, a binary image was first retrieved, and then the ice edges were quantified using a Canny edge detector. To evaluate the effectiveness of the proposed method, we applied it to SAR data from a challenging environment, including the terrain, ice, and ocean. The accuracy of the proposed dual-pol matrix and the SOA single-pol matrix were meticulously compared with the S2 data (ground-truth reference). Quantitative findings demonstrate the validity of the proposed method for ice cover extraction using Sentinel-1 data. Finally, retreat velocity estimation is important for meteorological pursuits.