Change search
ReferencesLink to record
Permanent link

Direct link
Classification of different types of snow using spectral and angular imaging
Luleå University of Technology, Department of Engineering Sciences and Mathematics.ORCID iD: 0000-0002-5943-1476
2016 (English)Licentiate thesis, comprehensive summary (Other academic)Alternative title
Klassificering av snö med hjälp av spektral och vinkel avbildning (Swedish)
Abstract [en]

The current thesis work details a non-contact detection approach concerningclassification of snow with different physical properties such as grain size, densityand specific surface area (SSA). In this approach, reflected light from snowsurfaces is measured as a function of wavelength and viewing geometry. Essentiallya detector (either a near-infrared (NIR) camera or a spectrometer) and anillumination source are needed to measure the spectrally and angularly resolvedbidirectional reflectance from snow. Classification of snow types is performedbased on the absorption and scattering properties of a respective snow type. Itis furthermore known that snow properties can be modelled using a numericalsolver where the radiative transfer equation (RTE) for snow is solved and ascattering phase function is estimated by expanding into a series of Legendrecoefficients. It is therefore expected to be a connection between snow characteristicsand the Legendre coefficients of the scattering phase function.

Results suggest that different snow types can be classified using two wavelengths(980 nm, 1310 nm) from the high reflectance region and one wavelength(1550 nm) from the high absorption region. It is also observed that thebidirectional reflectance for snow tends to increase in specular direction (antiilluminationdirection) as snow density increases. Results from the numericalmethod suggest that the first coefficient of the Legendre phase function is arelative estimate of the single scattering albedo rather than an absolute estimateand that the second coefficient estimates the anisotropy of a respectivesnow type. Investigations in this thesis suggest that the presented approachcan be used as a tool to classify different snow types in various applicationssuch as icing on wind turbine blades, winter roads maintenance and ski tracksmaintenance.v

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2016.
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Research subject
Experimental Mechanics
URN: urn:nbn:se:ltu:diva-59817ISBN: 978-91-7583-729-1 (print)ISBN: 978-91-7583-730-7 (electronic)OAI: diva2:1038492
2016-12-15, E243, Luleå University of Technology, Luleå, 10:00
Available from: 2016-10-18 Created: 2016-10-18 Last updated: 2016-12-19Bibliographically approved

Open Access in DiVA

fulltext(5720 kB)24 downloads
File information
File name FULLTEXT01.pdfFile size 5720 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Eppanapelli, Lavan Kumar
By organisation
Department of Engineering Sciences and Mathematics

Search outside of DiVA

GoogleGoogle Scholar
Total: 24 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 231 hits
ReferencesLink to record
Permanent link

Direct link