Blind Sparse Nonlinear Hyperspectral Unmixing Using an [Formula Omitted] Penalty

Blind hyperspectral unmixing (HU) is the task of jointly estimating the spectral signatures of materials and abundances in hyperspectral images. Most unmixing algorithms assume the linear mixture model, however, nonlinear models have recently gained interest, as they represent more complicated scene...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2018-01, Vol.15 (12), p.1907
Main Authors: Sigurdsson, J, Ulfarsson, M O, Sveinsson, J R
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Hyperspectral imaging
Mathematical models
Spectral signatures
Online Access:Get full text
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Summary:Blind hyperspectral unmixing (HU) is the task of jointly estimating the spectral signatures of materials and abundances in hyperspectral images. Most unmixing algorithms assume the linear mixture model, however, nonlinear models have recently gained interest, as they represent more complicated scenes. This letter proposes two blind nonlinear HU algorithms. The former algorithm assumes that the spectra are mixed according to the generalized bilinear model, while the latter assumes an extension to this model, called the Fan model. Both the algorithms use the ℓq regularizer to promote sparse abundances and solve the minimization problems using cyclic descent. The algorithms are evaluated and compared with other unmixing algorithms using both simulated and real data.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2018.2862895