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.| File | Dimensione | Formato | |
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