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Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data

INRS-Institut National de la Recherche Scientifique (INRS), Québec, QC G1K 9A9, Canada
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Academic Editors: Marc Brecht and Gwanggil Jeon
Sensors 2021, 21(16), 5259; https://doi.org/10.3390/s21165259
Received: 5 May 2021 / Revised: 27 July 2021 / Accepted: 31 July 2021 / Published: 4 August 2021
(This article belongs to the Section Optical Sensors)
This paper proposes an innovative method for classifying the physical properties of the seasonal snowpack using near-infrared (NIR) hyperspectral imagery to discriminate the optical classes of snow at different degrees of metamorphosis. This imaging system leads to fast and non-invasive assessment of snow properties. Indeed, the spectral similarity of two samples indicates the similarity of their chemical composition and physical characteristics. This can be used to distinguish, without a priori recognition, between different classes of snow solely based on spectral information. A multivariate data analysis approach was used to validate this hypothesis. A principal component analysis (PCA) was first applied to the NIR spectral data to analyze field data distribution and to select the spectral range to be exploited in the classification. Next, an unsupervised classification was performed on the NIR spectral data to select the number of classes. Finally, a confusion matrix was calculated to evaluate the accuracy of the classification. The results allowed us to distinguish three snow classes of typical shape and size (weakly, moderately, and strongly metamorphosed snow). The evaluation of the proposed approach showed that it is possible to classify snow with a success rate of 85% and a kappa index of 0.75. This illustrates the potential of NIR hyperspectral imagery to distinguish between three snow classes with satisfactory success rates. This work will open new perspectives for the modelling of physical parameters of snow using spectral data. View Full-Text
Keywords: seasonal snowpack; metamorphosis; classification; hyperspectral imaging; near-infrared seasonal snowpack; metamorphosis; classification; hyperspectral imaging; near-infrared
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MDPI and ACS Style

El Oufir, M.K.; Chokmani, K.; El Alem, A.; Agili, H.; Bernier, M. Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data. Sensors 2021, 21, 5259. https://doi.org/10.3390/s21165259

AMA Style

El Oufir MK, Chokmani K, El Alem A, Agili H, Bernier M. Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data. Sensors. 2021; 21(16):5259. https://doi.org/10.3390/s21165259

Chicago/Turabian Style

El Oufir, Mohamed K., Karem Chokmani, Anas El Alem, Hachem Agili, and Monique Bernier. 2021. "Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data" Sensors 21, no. 16: 5259. https://doi.org/10.3390/s21165259

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