Temporally Consistent Snow Cover Estimation from Noisy, Irregularly Sampled Measurements

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.

Published in:
Proc. 9th International Conference on Computer Vision Theory and Applications (VISAPP)
Presented at:
9th International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal, January 5-8, 2014
for time laps videos and a link to the perma sense data base, see: http://ivrg.epfl.ch/research/snow_segmentation

 Record created 2014-01-09, last modified 2018-03-17

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