Reprint
Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences
Edited by
May 2021
218 pages
- ISBN978-3-0365-0878-8 (Hardback)
- ISBN978-3-0365-0879-5 (PDF)
This is a Reprint of the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences that was published in
Engineering
Environmental & Earth Sciences
Summary
The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
hyperspectral; topographic correction; atmospheric correction; radiometric correction; long-range; long-distance; Structure from Motion (SfM); photogrammetry; mineral mapping; minimum wavelength mapping; Maarmorilik; Riotinto; Hyperspectral image; atmospheric correction; bio-optical algorithm; phycocyanin; chlorophyll-a; mangrove species classification; close-range hyperspectral imaging; field hyperspectral measurement; waveband selection; machine learning; instrument development; hyperspectral; spectroradiometry; telescope; receiver; soil; soil salinity; unmanned aerial vehicle; hyperspectral imager; random forest regression; electromagnetic induction; hyperspectral imaging; tree species; multiple classifier fusion; convolutional neural network; random forest; rotation forest; sea ice; ice algae; biomass; hyperspectral imaging; fine-scale; photogrammetry; under-ice; underwater; antarctica; structure from motion; georectification; mosaicking; push-broom; UAV; hyperspectral imaging; chlorophyll a; colored dissolved organic matter; in situ measurements; vertical distribution; water column; snapshot hyperspectral imaging; n/a