Linear and Nonlinear Unmixing in Hyperspectral Imaging

Nicolas Dobigeon, Yoann Altmann, N. Brun, S. Moussaoui

Research output: Chapter in Book/Report/Conference proceedingChapter

19 Citations (Scopus)

Abstract

Mainly due to the limited spatial resolution of the data acquisition devices, hyperspectral image pixels generally result from the mixture of several components that are present in the observed surface. Spectral mixture analysis (or spectral unmixing) is a key processing step which aims at identifying the spectral signatures of these materials and quantifying their spatial distribution over the image. The main purpose of this chapter is to introduce the spectral unmixing problem and to discuss some linear and nonlinear models and algorithms used to solve it. We will show that, capitalizing on several decades of methodological developments in the geoscience and remote sensing community, most of the unmixing algorithms proposed to unmix remotely sensed images can be directly applied in the chemometrics field to process hyperspectral data arising from various scanning microscopic techniques such as scanning transmission electron microscopy and Raman imaging.

Original languageEnglish
Title of host publicationResolving Spectral Mixtures With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging, 2016
EditorsCyril Ruckebusch
PublisherElsevier
Pages185-224
Number of pages40
Volume30
ISBN (Print)9780444636386
DOIs
Publication statusPublished - 2016

Publication series

NameData Handling in Science and Technology
PublisherElsevier
Volume30
ISSN (Print)0922-3487

Keywords

  • Abundance estimation
  • Endmember extraction
  • Hyperspectral imagery
  • Spectral unmixing

ASJC Scopus subject areas

  • Signal Processing
  • Chemistry(all)
  • Modelling and Simulation
  • Computer Science Applications

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  • Cite this

    Dobigeon, N., Altmann, Y., Brun, N., & Moussaoui, S. (2016). Linear and Nonlinear Unmixing in Hyperspectral Imaging. In C. Ruckebusch (Ed.), Resolving Spectral Mixtures With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging, 2016 (Vol. 30, pp. 185-224). (Data Handling in Science and Technology; Vol. 30). Elsevier. https://doi.org/10.1016/B978-0-444-63638-6.00006-1