Spectral Unmixing with Sparsity and Structuring Constraints

Mhenni, Ramzi Ben; Bourguignon, Sébastien; Ninin, Jordan; Schmidt, Frédéric

doi:10.1109/whispers.2018.8747046

Cited by 5 publications

(2 citation statements)

References 13 publications

(28 reference statements)

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“…Another work considered a mixed integer linear program (MILP) reformulation of the MESMA problem. This approach allows for a more efficient computation of an exact solution to (6) for small to medium scale problems [89].…”

Section: Intrinsic Spectral Variabilitymentioning

confidence: 99%

See 1 more Smart Citation

Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

Borsoi

Imbiriba

Bermudez

et al. 2021

IEEE Geosci. Remote Sens. Mag.

156

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The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process and compromises the quality of its results. Therefore, large efforts have been recently dedicated to mitigate the effects of spectral variability in SU. This resulted in the development of algorithms that incorporate different strategies to allow the EMs to vary within an HI, using, for instance, sets of spectral signatures known a priori, Bayesian, parametric, or local EM models. Each of these approaches has different characteristics and underlying motivations. This paper presents a comprehensive literature review contextualizing both classic and recent approaches to solve this problem. We give a detailed evaluation of the sources of spectral variability and their effect in HI spectra. Furthermore, we propose a new taxonomy that organizes existing work according to a practitioner's point of view, based on the necessary amount of supervision and on the computational cost they require. We also review methods used to construct spectral libraries (which are required by many SU techniques) based on the observed HI, as well as algorithms for library augmentation and reduction. Finally, we conclude the paper with some discussions and an outline of possible future directions for the field.

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Section: Intrinsic Spectral Variabilitymentioning

confidence: 99%

“…where • 0 is the L 0 pseudo-norm, which counts the number of non-zero elements in a vector. Note that (8) would be equivalent to MESMA if we just added an additional linear structuring constraint to enforce the occurrence of only a single nonzero abundance per material class [89].…”

Section: Sparse Unmixingmentioning

confidence: 99%