Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

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 la...

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Bibliographic Details
Other Authors: Vohland, Michael (Editor), Jung, András (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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DOAB: description of the publication
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245 1 0 |a Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences 
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520 |a 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. 
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650 7 |a Research & information: general  |2 bicssc 
653 |a hyperspectral 
653 |a topographic correction 
653 |a atmospheric correction 
653 |a radiometric correction 
653 |a long-range 
653 |a long-distance 
653 |a Structure from Motion (SfM) 
653 |a photogrammetry 
653 |a mineral mapping 
653 |a minimum wavelength mapping 
653 |a Maarmorilik 
653 |a Riotinto 
653 |a Hyperspectral image 
653 |a bio-optical algorithm 
653 |a phycocyanin 
653 |a chlorophyll-a 
653 |a mangrove species classification 
653 |a close-range hyperspectral imaging 
653 |a field hyperspectral measurement 
653 |a waveband selection 
653 |a machine learning 
653 |a instrument development 
653 |a spectroradiometry 
653 |a telescope 
653 |a receiver 
653 |a soil 
653 |a soil salinity 
653 |a unmanned aerial vehicle 
653 |a hyperspectral imager 
653 |a random forest regression 
653 |a electromagnetic induction 
653 |a hyperspectral imaging 
653 |a tree species 
653 |a multiple classifier fusion 
653 |a convolutional neural network 
653 |a random forest 
653 |a rotation forest 
653 |a sea ice 
653 |a ice algae 
653 |a biomass 
653 |a fine-scale 
653 |a under-ice 
653 |a underwater 
653 |a antarctica 
653 |a structure from motion 
653 |a georectification 
653 |a mosaicking 
653 |a push-broom 
653 |a UAV 
653 |a chlorophyll a 
653 |a colored dissolved organic matter 
653 |a in situ measurements 
653 |a vertical distribution 
653 |a water column 
653 |a snapshot hyperspectral imaging 
653 |a n/a 
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