Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability

Traditional hyperspectral unmixing methods neglect the underlying variability of spectral signatures often observed in typical hyperspectral images (HI), propagating these mismodeling errors throughout the whole unmixing process. Attempts to model material spectra as members of sets or as random var...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 58; H. 3; S. 1833 - 1842
Hauptverfasser:	Imbiriba, Tales, Borsoi, Ricardo Augusto, Bermudez, Jose Carlos Moreira
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Additives Algorithms Computer simulation Electrical engineering Endmember (EM) variability Environmental management hyperspectral data Hyperspectral imaging Ill posed problems low rank Parametric statistics Random variables Regularization Spectra Spectral signatures tensor decomposition Tensors ULTRA accounting for EM variability (ULTRA-V) unmixing with low-rank tensor regularization algorithm (ULTRA) Variability
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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