Cite as:
Lehnert, L.W.; Meyer, H.; Obermeier, W.; Silva, B.; Regeling, B.; Thies, B. &amp; Bendix, J. (2016): <b>Hyperspectral data analysis in R: the hsdar-package</b>. <i>Journal of Statistical Software</i> <b>online</b>, online.

Resource Description

Title: Hyperspectral data analysis in R: the hsdar-package
F2Fdw ID: 61
Publication Date: 2016-09-10
License and Usage Rights: FACE2FACE data user agreement.
Resource Owner(s):
Individual: Lehnert, Lukas W.
Individual: Meyer, Hanna
Individual: Obermeier, Wolfgang
Individual: Silva, Brenner
Individual: Regeling, Bianca
Individual: Thies, Boris
Individual: Bendix, Jörg
Hyperspectral remote sensing is a promising tool for a variety of applications including ecology and geology but also analytical chemistry and medical research. Here, the new hsdar-package for R statistical software is presented, which allows to perform a large variety of analysis steps during a typical hyperspectral remote sensing approach. Therefore, the package introduces a new class to e?ciently store even large hyperspectral datasets such as hyperspectral cubes within R. The package includes several important hyperspectral analysis tools such as continuum removal, normalized ratio indices and integrates two widely used radiation transfer models. Besides this, the package provides methods to directly use the functionality of the caret-package for machine learning tasks. To demonstrate the range of functions of the hsdar-package, two case studies are included. The ?rst one shows the estimation of plant leave chlorophyll content and the second one the ability to detect cancer in the human larynx from hyperspectral data.<br/>
| Hyperspectral remote sensing | hyperspectral imaging | spectroscopy | continuum removal | normalized ratio indices |
Literature type specific fields:
Journal: Journal of Statistical Software
Volume: online
Page Range: online
Publisher: American Statistical Association
Metadata Provider:
Individual: Yuan, Naiming
Online Distribution:
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