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Authors: Dominic Rüfenacht 1 ; Matthew Brown 2 ; Jan Beutel 3 and Sabine Süsstrunk 1

Affiliations: 1 EPFL, Switzerland ; 2 University of Bath, United Kingdom ; 3 ETH Zurich, Switzerland

Keyword(s): Surface Classification, Gaussian Mixture Models of Color, Markov Random Fields.

Abstract: We propose a method for accurate and temporally consistent surface classification in the presence of noisy, irregularly sampled measurements, and apply it to the estimation of snow coverage over time. The input imagery is extremely challenging, with large variations in lighting and weather distorting the measurements. Initial snow cover estimations are obtained using a Gaussian Mixture Model of color. To achieve a temporally consistent snow cover estimation, we use a Markov Random Field that penalizes rapid fluctuations in the snow state, and show that the penalty term needs to be quite large, resulting in slow reactivity to changes. We thus propose a classifier to separate good from uninformative images, which allows to use a smaller penalty term. We show that the incorporation of domain knowledge to discard uninformative images leads to better reactivity to changes in snow coverage as well as more accurate snow cover estimations.

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Paper citation in several formats:
Rüfenacht, D.; Brown, M.; Beutel, J. and Süsstrunk, S. (2014). Temporally Consistent Snow Cover Estimation from Noisy, Irregularly Sampled Measurements. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP; ISBN 978-989-758-004-8; ISSN 2184-4321, SciTePress, pages 275-283. DOI: 10.5220/0004657202750283

@conference{visapp14,
author={Dominic Rüfenacht. and Matthew Brown. and Jan Beutel. and Sabine Süsstrunk.},
title={Temporally Consistent Snow Cover Estimation from Noisy, Irregularly Sampled Measurements},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP},
year={2014},
pages={275-283},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004657202750283},
isbn={978-989-758-004-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP
TI - Temporally Consistent Snow Cover Estimation from Noisy, Irregularly Sampled Measurements
SN - 978-989-758-004-8
IS - 2184-4321
AU - Rüfenacht, D.
AU - Brown, M.
AU - Beutel, J.
AU - Süsstrunk, S.
PY - 2014
SP - 275
EP - 283
DO - 10.5220/0004657202750283
PB - SciTePress

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