Linear and Nonlinear Unmixing in Hyperspectral Imaging

@inproceedings{Dobigeon2016LinearAN,
  title={Linear and Nonlinear Unmixing in Hyperspectral Imaging},
  author={Nicolas Dobigeon and Yoann Altmann and Nathalie Brun and Sa{\"i}d Moussaoui},
  year={2016}
}
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… CONTINUE READING
BETA

Similar Papers

Citations

Publications citing this paper.
SHOWING 1-9 OF 9 CITATIONS

A MAP-Based Approach for Hyperspectral Imagery Super-Resolution

  • IEEE Transactions on Image Processing
  • 2018
VIEW 6 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Bandbereichswahl und Materialanteilsschätzung mithilfe von Spektralfiltern

Wolfgang Krippner, Fernando Puente León
  • 2018
VIEW 4 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Spectral Unmixing With Perturbed Endmembers

  • IEEE Transactions on Geoscience and Remote Sensing
  • 2019
VIEW 1 EXCERPT
CITES BACKGROUND

Considering spectral variability for optical material abundance estimation

Wolfgang Krippner, Sebastian Bauer, Fernando Puente León
  • 2017
VIEW 1 EXCERPT
CITES BACKGROUND

References

Publications referenced by this paper.
SHOWING 1-10 OF 185 REFERENCES

Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization

  • IEEE Transactions on Image Processing
  • 2015
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Spectral mixture analysis of EELS spectrum-images.

  • Ultramicroscopy
  • 2012
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Principal Component Analysis

  • International Encyclopedia of Statistical Science
  • 2011
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Robust Linear Spectral Unmixing Using Anomaly Detection

  • IEEE Transactions on Computational Imaging
  • 2015
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

A Bilinear–Bilinear Nonnegative Matrix Factorization Method for Hyperspectral Unmixing

  • IEEE Geoscience and Remote Sensing Letters
  • 2014
VIEW 2 EXCERPTS
HIGHLY INFLUENTIAL