LEADER 04257nam 2201033z- 450 001 9910557368003321 005 20220111 035 $a(CKB)5400000000042211 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76303 035 $a(oapen)doab76303 035 $a(EXLCZ)995400000000042211 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aHyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (218 p.) 311 08$a3-0365-0878-3 311 08$a3-0365-0879-1 330 $aThe 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. 606 $aResearch and information: general$2bicssc 610 $aantarctica 610 $aatmospheric correction 610 $abio-optical algorithm 610 $abiomass 610 $achlorophyll a 610 $achlorophyll-a 610 $aclose-range hyperspectral imaging 610 $acolored dissolved organic matter 610 $aconvolutional neural network 610 $aelectromagnetic induction 610 $afield hyperspectral measurement 610 $afine-scale 610 $ageorectification 610 $ahyperspectral 610 $aHyperspectral image 610 $ahyperspectral imager 610 $ahyperspectral imaging 610 $aice algae 610 $ain situ measurements 610 $ainstrument development 610 $along-distance 610 $along-range 610 $aMaarmorilik 610 $amachine learning 610 $amangrove species classification 610 $amineral mapping 610 $aminimum wavelength mapping 610 $amosaicking 610 $amultiple classifier fusion 610 $an/a 610 $aphotogrammetry 610 $aphycocyanin 610 $apush-broom 610 $aradiometric correction 610 $arandom forest 610 $arandom forest regression 610 $areceiver 610 $aRiotinto 610 $arotation forest 610 $asea ice 610 $asnapshot hyperspectral imaging 610 $asoil 610 $asoil salinity 610 $aspectroradiometry 610 $astructure from motion 610 $aStructure from Motion (SfM) 610 $atelescope 610 $atopographic correction 610 $atree species 610 $aUAV 610 $aunder-ice 610 $aunderwater 610 $aunmanned aerial vehicle 610 $avertical distribution 610 $awater column 610 $awaveband selection 615 7$aResearch and information: general 700 $aVohland$b Michael$4edt$01294154 702 $aJung$b Andra?s$4edt 702 $aVohland$b Michael$4oth 702 $aJung$b Andra?s$4oth 906 $aBOOK 912 $a9910557368003321 996 $aHyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences$93022940 997 $aUNINA