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N-FINDR, Vertex Component Analysis (VCA) and Iterative Constrained Endmember (ICE) algorithms for hyperspectral unmixing in R.

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unmixR: An R Package for Unmixing of Hyperspectral Images

R-CMD-check Test coverage Codecov test coverage Website (pkgdown) Lifecycle: experimental Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.

This README is for GitHub only. pkgdown website currently has a separate README.

unmixR is WORK IN PROGRESS.


The fundamental structures & behavior may change. For the time being, use at your own risk.

Hyperspectral data are also called 'imaging spectroscopy' and 'imaging spectrometer data' depending upon the discipline. Such data consists of spectra collected over an x, y grid. Data sets like this are found in airborne land imaging studies, biomedical studies and art history investigations. The spectra are often visible, infrared, near-infrared, raman spectra or mass spectrometer data sets.

Installation

Installation: works easiest using remotes::install_git():

library("remotes")
remotes::install_github("r-hyperspec/unmixR", subdir = "pkg/unmixR")

Acknoledgements

Development of unmixR has been supported by Google Summer of Code 2013 (Conor McManus) and 2016 (Anton Belov). Thank you Google!

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N-FINDR, Vertex Component Analysis (VCA) and Iterative Constrained Endmember (ICE) algorithms for hyperspectral unmixing in R.

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