Download
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.
License
Creative Commons Attribution LicenseFollow Hyperspectral data analysis in R
Other Useful Business Software
Gen AI apps are built with MongoDB Atlas
MongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of Hyperspectral data analysis in R!