Hyperspectral videos—multi-wavelength imaging of objects over time—generate a lot of informative data. But such diffuse spectroscopy measurements are usually non-selective, i.e., they respond to many different phenomena at the same time. To become quantitative, reliable and understandable, they require efficient mathematical data modeling. This article concerns how to model both known and unknown variation types in hyperspectral video data.

4.16 - Fast Analysis, Processing and Modeling of Hyperspectral Videos: Challenges and Possible Solutions / Vitale, R.; Stefansson, P.; Marini, F.; Ruckebusch, C.; Burud, I.; Martens, H.. - (2020), pp. 395-409. [10.1016/B978-0-12-409547-2.14605-0].

4.16 - Fast Analysis, Processing and Modeling of Hyperspectral Videos: Challenges and Possible Solutions

Marini F.;
2020

Abstract

Hyperspectral videos—multi-wavelength imaging of objects over time—generate a lot of informative data. But such diffuse spectroscopy measurements are usually non-selective, i.e., they respond to many different phenomena at the same time. To become quantitative, reliable and understandable, they require efficient mathematical data modeling. This article concerns how to model both known and unknown variation types in hyperspectral video data.
2020
Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Second Edition: Four Volume Set
Big Data; Extended Multiplicative Signal Correction (EMSC); Hyperspectral videos; Known/unknown variation sources; On-The-Fly Processing (OTFP)
02 Pubblicazione su volume::02a Capitolo o Articolo
4.16 - Fast Analysis, Processing and Modeling of Hyperspectral Videos: Challenges and Possible Solutions / Vitale, R.; Stefansson, P.; Marini, F.; Ruckebusch, C.; Burud, I.; Martens, H.. - (2020), pp. 395-409. [10.1016/B978-0-12-409547-2.14605-0].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1687658
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