Data reduction by randomization subsampling for the study of large hyperspectral datasets

Large amount of information in hyperspectral images (HSI) generally makes their analysis (e.g., principal component analysis, PCA) time consuming and often requires a lot of random access memory (RAM) and high computing power. This is particularly problematic for analysis of large images, containing...

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Veröffentlicht in:	Analytica chimica acta 2022-05, Vol.1209, p.339793-339793, Article 339793
Hauptverfasser:	Cruz-Tirado, J.P., Amigo, José Manuel, Barbin, Douglas Fernandes, Kucheryavskiy, Sergey
Format:	Artikel
Sprache:	eng
Schlagworte:	Data reduction Hyperspectral imaging Principal Component Analysis Random Allocation Randomization Sub-sampling Time series
Online-Zugang:	Volltext
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