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Synthetic Aperture Radar (SAR) imagery is the primary data type used for seaice mapping due to its spatio-temporal coverage and the ability to detect seaice independent of cloud and lighting conditions. Automatic sea ice detectionusing SAR imagery remains problematic due to the presence of ambiguous signaland noise within the image. Conversely, ice and water are easilydistinguishable using multispectral imagery (MSI), but in the polar regions theocean's surface is often occluded by cloud or the sun may not appear above thehorizon for many months. To address some of these limitations, this paperproposes a new tool trained using concurrent multispectral Visible and SARimagery for sea Ice Detection (ViSual\_IceD). ViSual\_IceD is a convolutionneural network (CNN) that builds on the classic U-Net architecture bycontaining two parallel encoder stages, enabling the fusion and concatenationof MSI and SAR imagery containing different spatial resolutions. Theperformance of ViSual\_IceD is compared with U-Net models trained usingconcatenated MSI and SAR imagery as well as models trained exclusively on MSIor SAR imagery. ViSual\_IceD outperforms the other networks, with a F1 score1.60\% points higher than the next best network, and results indicate thatViSual\_IceD is selective in the image type it uses during image segmentation.Outputs from ViSual\_IceD are compared to sea ice concentration productsderived from the AMSR2 Passive Microwave (PMW) sensor. Results highlight howViSual\_IceD is a useful tool to use in conjunction with PMW data, particularlyin coastal regions. As the spatial-temporal coverage of MSI and SAR imagerycontinues to increase, ViSual\_IceD provides a new opportunity for robust,accurate sea ice coverage detection in polar regions.

Sea ice detection using concurrent multispectral and synthetic aperture radar imagery