Hyperspectral Data Analysis in R: the hsdar Package

TL;DR

The hsdar package for R provides comprehensive tools for hyperspectral data analysis, including data storage, spectral indices, radiation transfer models, and machine learning integration, demonstrated through ecological and medical case studies.

Contribution

This paper introduces the hsdar package, a new R tool that streamlines hyperspectral data analysis with novel data structures and integrated analysis methods.

Findings

Effective estimation of plant chlorophyll content

Successful detection of human larynx cancer

Demonstrated versatility across ecological and medical applications

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 datasets 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.