  As Arctic conditions rapidly change, human activity in the Arctic will
continue to increase and so will the need for high-resolution observations of
sea ice. While satellite imagery can provide high spatial resolution, it is
temporally sparse and significant ice deformation can occur between
observations. This makes it difficult to apply feature tracking or image
correlation techniques that require persistent features to exist between
images. With this in mind, we propose a technique based on optimal transport,
which is commonly used to measure differences between probability
distributions. When little ice enters or leaves the image scene, we show that
regularized optimal transport can be used to quantitatively estimate ice
deformation. We discuss the motivation for our approach and describe efficient
computational implementations. Results are provided on a combination of
synthetic and MODIS imagery to demonstrate the ability of our approach to
estimate dynamics properties at the original image resolution.
