Cite as:
Lehnert, L.; Meyer, H.; Obermeier, W.; Silva, B.; Regeling, B.; Thies, B. &amp; Bendix, J. (2019): <b>Hyperspectral Data Analysis in R: The hsdar Package</b>. <i>Journal of Statistical Software</i> <b>89</b>(12), 1-23.

Resource Description

Title: Hyperspectral Data Analysis in R: The hsdar Package
FOR816dw ID: 334
Publication Date: 2019-06-05
License and Usage Rights:
Resource Owner(s):
Individual: Lukas Lehnert
Individual: Hanna Meyer
Individual: Wolfgang Obermeier
Individual: Brenner Silva
Individual: Bianca Regeling
Individual: Boris Thies
Individual: Jörg Bendix
Hyperspectral remote sensing is a promising tool for a variety of applications including<br/> ecology, geology, analytical chemistry and medical research. This article presents the new<br/> hsdar package for R statistical software, which performs a variety of analysis steps taken<br/> during a typical hyperspectral remote sensing approach. The package introduces a new<br/> class for efficiently storing large hyperspectral data sets such as hyperspectral cubes within<br/> R. The package includes several important hyperspectral analysis tools such as continuum<br/> removal, normalized ratio indices and integrates two widely used radiation transfer models.<br/> In addition, the package provides methods to directly use the functionality of the caret<br/> package for machine learning tasks. Two case studies demonstrate the package’s range of<br/> functionality: First, plant leaf chlorophyll content is estimated and second, cancer in the<br/> human larynx is detected from hyperspectral data.
| hyperspectral remote sensing | R code |
Literature type specific fields:
Journal: Journal of Statistical Software
Volume: 89
Issue: 12
Page Range: 1-23
Metadata Provider:
Individual: Jörg Bendix
Online Distribution:
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