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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2020-115
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-115
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  28 Jul 2020

28 Jul 2020

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This preprint is currently under review for the journal TC.

Rock and snow differentiation from colour (RGB) images

Alex Burton-Johnson1 and Nina Sofia Wyniawskyj2 Alex Burton-Johnson and Nina Sofia Wyniawskyj
  • 1British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 OET, UK
  • 2Deimos Space UK Ltd., Building R103, Fermi Avenue, Harwell, OX11 0QR, UK

Abstract. We present a new method for differentiating snow and rock in colour imagery for application (including by remote sensing non-specialists) to multidisciplinary geospatial analyses in the Polar Regions (e.g. glaciology, geology, and biology). Existing methods for differentiating rock from snow and ice for land cover analysis in the Polar Regions rely on infrared or near-infrared imagery (e.g. the Normalised Difference Snow Index, NDSI). However, colour images are more abundant and higher resolution. To enable application of this resource, we present and review supervised and unsupervised methods for differentiating rock and snow from colour images. Whilst the unsupervised methods (fuzzy membership and a normalised difference index) are unable to accurately differentiate snow and rock from colour images, supervised classification (Maximum Likelihood Classification (MLC) and a new approach, Polynomial Thresholding (PT)) do achieve high classification accuracies (95 ± 2 % for PT and 94 ± 3 % for MLC, compared with manual delineation). The greater user control of PT achieves better accuracies than MLC in shaded areas (a challenge in high latitudes) and less extensive outcrops. We present the workflow for the new PT method, and provide a calibration tool for its implementation. This approach improves the possible resolution of Polar land cover analysis, and the increases the volume of data that can be utilised.

Alex Burton-Johnson and Nina Sofia Wyniawskyj

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Alex Burton-Johnson and Nina Sofia Wyniawskyj

Alex Burton-Johnson and Nina Sofia Wyniawskyj

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Latest update: 04 Aug 2020
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Short summary
Accurate maps of Polar Regions are vital for navigation and scientific research. However, automated mapping of snow and rock requires low resolution infrared imagery. This is the first paper to evaluate mapping rocks and snow from colour imagery, and presents a new methodology. The techniques are evaluated, and shown to have high accuracy. By allowing usage of high resolution and abundant colour imagery we hope to improve Polar mapping and geospatial research in diverse disciplines.
Accurate maps of Polar Regions are vital for navigation and scientific research. However,...
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