Lehnert, L.; Meyer, H.; Obermeier, W.; Silva, B.; Regeling, B.; Thies, B. & 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
email:
bendix <at> staff.uni-marburg.de
Deutschhausstraße 12
Room No. 02 A 48
35032 Marburg
Faculty of Geography
Germany
Abstract:
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.
Keywords:
| hyperspectral remote sensing | R code |
Literature type specific fields:
ARTICLE
Journal:
Journal of Statistical Software
Volume:
89
Issue:
12
Page Range:
1-23
Metadata Provider:
Individual:
Jörg Bendix
Contact:
email:
bendix <at> staff.uni-marburg.de
Deutschhausstraße 12
Room No. 02 A 48
35032 Marburg
Faculty of Geography
Germany
