Articles | Volume 15, issue 11
https://doi.org/10.5194/tc-15-5041-2021
https://doi.org/10.5194/tc-15-5041-2021
Research article
 | 
01 Nov 2021
Research article |  | 01 Nov 2021

Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods

Melanie Marochov, Chris R. Stokes, and Patrice E. Carbonneau

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Cited articles

Alifu, H., Tateishi, R., and Johnson, B.: A new band ratio technique for mapping debris-covered glaciers using Landsat imagery and a digital elevation model, Int. J. Remote Sens., 36, 2063–2075, https://doi.org/10.1080/2150704X.2015.1034886, 2015. 
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Andresen, C. S., Straneo, F., Ribergaard, M. H., Bjørk, A. A., Andersen, T. J., Kuijpers, A., Nørgaard-Pedersen, N., Kjær, K. H., Schjøth, F., Weckström, K., and Ahlstrøm, A. P.: Rapid response of Helheim Glacier in Greenland to climate variability over the past century, Nat. Geosci., 5, 37–41, https://doi.org/10.1038/ngeo1349, 2012. 
Andresen, C. S., Sicre, M.-A., Straneo, F., Sutherland, D. A., Schmith, T., Hvid Ribergaard, M., Kuijpers, A., and Lloyd, J. M.: A 100-year long record of alkenone-derived SST changes by southeast Greenland, Cont. Shelf Res., 71, 45–51, https://doi.org/10.1016/j.csr.2013.10.003, 2013. 
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Short summary
Research into the use of deep learning for pixel-level classification of landscapes containing marine-terminating glaciers is lacking. We adapt a novel and transferable deep learning workflow to classify satellite imagery containing marine-terminating outlet glaciers in Greenland. Our workflow achieves high accuracy and mimics human visual performance, potentially providing a useful tool to monitor glacier change and further understand the impacts of climate change in complex glacial settings.
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