hyperspectral data analysis in r

We have hosted the application hyperspectral data analysis in r in order to run this application in our online workstations with Wine or directly.

Quick description about hyperspectral data analysis in r:

The hsdar package contains classes and functions to manage, analyse and simulate hyperspectral data. These might be either spectrometer measurements or hyperspectral images through the interface of rgdal.

Features:

• Data handling: hsdar is designed to handle even large sets of spectra. Spectra are stored in a speclib containing, amongst other details, the wavelength and reflectance for each spectrum. hsdar further contains functions for plotting spectral data (e.g. plot.speclib).
• Data manipulation: A variety of established methods for data manipulation such as filter func- tions (smooth.speclib), resampling of bands to various satellite sensors (spectral.resampling), continuum removal (transform_speclib), calculations of derivations (derivative.speclib) and extraction of absorption features (cut.specfeat) are implemented.
• Data analysis: Supported methods to analyse vegetation spectra are the calculation of red edge parameters (rededge), vegetation indices (vegindex) as well as ndvi-like narrow band indices (nri). hsdar further enables to perform spectral unmixing of spectra (unmix) by use of endmember spectra.
• Data simulation: hsdar has implemented the models PROSAIL 5B (PROSAIL, Jacquemoud et al. 2009)and PROSPECT 5 (PROSPECT, Jacquemoud and Baret 1990) to simulate spectra of canopy and plants.

Programming Language: Fortran, C, S/R.
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