  Synthetic Aperture Radar (SAR) imagery is the primary data type used for sea
ice mapping due to its spatio-temporal coverage and the ability to detect sea
ice independent of cloud and lighting conditions. Automatic sea ice detection
using SAR imagery remains problematic due to the presence of ambiguous signal
and noise within the image. Conversely, ice and water are easily
distinguishable using multispectral imagery (MSI), but in the polar regions the
ocean's surface is often occluded by cloud or the sun may not appear above the
horizon for many months. To address some of these limitations, this paper
proposes a new tool trained using concurrent multispectral Visible and SAR
imagery for sea Ice Detection (ViSual\_IceD). ViSual\_IceD is a convolution
neural network (CNN) that builds on the classic U-Net architecture by
containing two parallel encoder stages, enabling the fusion and concatenation
of MSI and SAR imagery containing different spatial resolutions. The
performance of ViSual\_IceD is compared with U-Net models trained using
concatenated MSI and SAR imagery as well as models trained exclusively on MSI
or SAR imagery. ViSual\_IceD outperforms the other networks, with a F1 score
1.60\% points higher than the next best network, and results indicate that
ViSual\_IceD is selective in the image type it uses during image segmentation.
Outputs from ViSual\_IceD are compared to sea ice concentration products
derived from the AMSR2 Passive Microwave (PMW) sensor. Results highlight how
ViSual\_IceD is a useful tool to use in conjunction with PMW data, particularly
in coastal regions. As the spatial-temporal coverage of MSI and SAR imagery
continues to increase, ViSual\_IceD provides a new opportunity for robust,
accurate sea ice coverage detection in polar regions.
