Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis

Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis

Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent hyperspectral image processing algorithms. This paper i...

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Bibliographic Details
Main Authors: Alina L. Machidon, Fabio Del Frate, Matteo Picchiani, Octavian M. Machidon, Petre L. Ogrutan
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Remote Sensing
Subjects:
hyperspectral image
dimensionality reduction
principal component analysis
land classification
Online Access:https://www.mdpi.com/2072-4292/12/11/1698

Internet

https://www.mdpi.com/2072-4292/12/11/1698

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