High-Resolution Sea Ice Maps with Convolutional Neural Networks

• Main Authors: Malmgren-Hansen, David, Nielsen, Allan Aasbjerg, Kreiner, Matilde Brandt, Saldo, Roberto, Skriver, Henning, Toudal Pedersen, Leif, Lavelle, John, Buus-Hinkler, Jørgen
• Format: Conference Object
• Language: English
• Published: 2019
• Subjects: /dk/atira/pure/sustainabledevelopmentgoals/climate_action; name=SDG 13 - Climate Action; /dk/atira/pure/sustainabledevelopmentgoals/life_below_water; name=SDG 14 - Life Below Water; Arctic; Sea ice
• Online Access: https://orbit.dtu.dk/en/publications/a240bd91-02f3-4eab-bec3-9ae2bf5f0448

Automatically generated high resolution sea ice maps have the potential to increase the use of satellite imagery in arctic applications. Applications include marine navigation, offshore operations, validation of ice models, and climate research. Especially for arctic marine navigation, frequent ice maps in high resolution are requested by most users, as documented by an internal project stakeholder survey. We present current results from our large-scale study of high resolution ice maps generation with Convolutional Neural Networks (CNNs). Our study is based on dual polarized (HH+HV) Extra Wide swath (EW) SAR data from the Copernicus Sentinel 1 satellite mission and we generate pixel-wise sea ice estimates in 40m x 40m resolution. The presentation will include a model validation against expert annotations of SAR images. In the near future we will expand our study to include AMSR2 Microwave Radiometer (MWR) data as input. The addition of MWR data can potentially solve the ambiguities in SAR data over open water, due to SAR backscatter variation at different wind conditions. Some CNN estimates are observed to confuse very homogeneous ice surfaces with similar backscatter open water scenarios, but results show a clear potential for this methodology. Our work is carried out under a Danish research project named Automated downstream Sea Ice Products (ASIP). The project goal is to automate generation of sea ice information from satellite images. ASIP is a collaboration between the Danish Meteorological Institute (DMI), the Technical University of Denmark and Harnvig Artic and Maritime. It sets out to automate, partially or fully, the extraction of arctic sea ice information from satellite imagery. Today, ice mapping is mainly done manually by ice-experts at national Ice Centers around the world. The project goal will enable analyzing larger quantities of satellite data, for better utilization of the available Sentinel-1 images and for providing ice maps to users more frequently. Recent literature shows an increased ...