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200 1 $aWavelets and Operators$fYves Meyer$gtranslated by D.H. Salinger
210   $aCambridge$cCambridge University Press$d1992
225 1 $aCambridge studies in advanced mathematics$v37
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702  1$aSalinger,$bDavid H.
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035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/49739
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100   $a20202102d2018      |y         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aHyperspectral Image Unmixing Incorporating Adjacency Information
210   $cKIT Scientific Publishing$d2018
215   $a1 online resource (XIII, 203 p. p.)
225 1 $aForschungsberichte aus der Industriellen Informationstechnik / Institut für Industrielle Informationstechnik (IIIT), Karlsruher Institut für Technologie
311 08$a3-7315-0788-9 
330   $aWhile the spectral information contained in hyperspectral images is rich, the spatial resolution of such images is in many cases very low. Many pixel spectra are mixtures of pure materials' spectra and therefore need to be decomposed into their constituents. This work investigates new decomposition methods taking into account spectral, spatial and global 3D adjacency information. This allows for faster and more accurate decomposition results.
606   $aTechnology: general issues$2bicssc
610   $ablind source separation
610   $aBlinde Quellentrennung
610   $aHyperspectral image processing
610   $aHyperspektrale Bildverarbeitung
610   $aNichtnegative Matrixzerlegung
610   $anonnegative matrix factorization
610   $aspectral unmixing
610   $aSpektrale Entmischung
615  7$aTechnology: general issues
700   $aBauer$b Sebastian$4auth$01292408
906   $aBOOK
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996   $aHyperspectral Image Unmixing Incorporating Adjacency Information$93022273
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