The internal structure of the snow and its reflectance function play a major contribution in its appearance. We investigate the snow reflectance model introduced by Kokhanovsky and Zege in a close-range imaging scale. By monitoring the evolution of melting snow through time using
hyperspectral cameras in a laboratory, we estimate snow grain sizes from 0.24 to 8.49 mm depending on the grain shape assumption chosen. Using our experimental results, we observe differences in the reconstructed reflectance spectra with the model regarding the spectra's shape or magnitude.
Those variations may be due to our data or to the grain shape assumption of the model. We introduce an effective parameter describing both the snow grain size and the snow grain shape, to give us the opportunity to select the adapted assumption. The computational technique is ready, but more
ground truths are required to validate the model.
Journal Title : Color and Imaging Conference
Publisher Name : Society for Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151 USA
Mathieu Nguyen, Jean-Baptiste Thomas, Ivar Farup, "Investigating the Kokhanovsky snow reflectance model in closerange spectral imaging" in Proc. IS&T 29th Color and Imaging Conf.,2021,pp 31 - 36, https://doi.org/10.2352/issn.2169-2629.2021.29.31
Investigating the Kokhanovsky snow reflectance model in closerange spectral imaging
NguyenMathieu
ThomasJean-Baptiste
FarupIvar
01112021
2021
29
31
36
2021
The internal structure of the snow and its reflectance function play a major contribution in its appearance. We investigate the snow reflectance model introduced by Kokhanovsky and Zege in a close-range imaging scale. By monitoring the evolution of melting snow through time using
hyperspectral cameras in a laboratory, we estimate snow grain sizes from 0.24 to 8.49 mm depending on the grain shape assumption chosen. Using our experimental results, we observe differences in the reconstructed reflectance spectra with the model regarding the spectra's shape or magnitude.
Those variations may be due to our data or to the grain shape assumption of the model. We introduce an effective parameter describing both the snow grain size and the snow grain shape, to give us the opportunity to select the adapted assumption. The computational technique is ready, but more
ground truths are required to validate the model.