期刊
JOURNAL OF STATISTICAL SOFTWARE
卷 89, 期 12, 页码 -出版社
JOURNAL STATISTICAL SOFTWARE
DOI: 10.18637/jss.v089.i12
关键词
hyperspectral remote sensing; hyperspectral imaging; spectroscopy; continuum removal; normalized ratio indices
资金
- German Federal Ministry of Education and Research (BMBF) within the Pasture Degradation Monitoring System (PaDeMoS) project [03G0808C]
- Hessian State Ministry of Higher Education, Research and the Arts
- project Early Detection of Laryngeal Cancer by Hyperspectral Imaging (German Cancer Aid) [109825, 110275]
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.
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