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Hyperspectral Image Unmixing Incorporating Adjacency Information

While 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...

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Main Author: Bauer, Sebastian
Format: Online
Language:English
Published: KIT Scientific Publishing 2021
Subjects:
T1-995
Spektrale Entmischung
spectral unmixing
Blinde Quellentrennung
Hyperspectral image processing
Nichtnegative Matrixzerlegung
nonnegative matrix factorization
blind source separation
Hyperspektrale Bildverarbeitung
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
Online Access:34233
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Summary:While 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.

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