Abstract:
Hyperspectral remote sensing is a promising tool for a variety of applications including
ecology, geology, analytical chemistry and medical research. This article presents the new
hsdar package for R statistical software, which performs a variety of analysis steps taken
during a typical hyperspectral remote sensing approach. The package introduces a new
class for efficiently storing large hyperspectral data sets 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.
In addition, the package provides methods to directly use the functionality of the caret
package for machine learning tasks. Two case studies demonstrate the package’s range of
functionality: First, plant leaf chlorophyll content is estimated and second, cancer in the
human larynx is detected from hyperspectral data.
