Nonlinear hyperspectral unmixing using Gaussian processes

Y. Altmann, N. Dobigeon, J.-Y. Tourneret, S. McLaughlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method estimates the abundance vectors for all the image pixels using a Gaussian process latent variable model. The endmembers are subsequently estimated using Gaussian process regression. The performance of the unmixing strategy is compared with state-of-the-art unmixing strategies on synthetic data. An interesting property is its robustness to the absence of pure pixels in the image.

Original languageEnglish
Title of host publication2013 5th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing (WHISPERS)
ISBN (Electronic)9781509011193
Publication statusPublished - Jun 2013
Event5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing 2013 - Gainesville, United States
Duration: 26 Jun 201328 Jun 2013

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
ISSN (Print)2158-6276


Conference5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing 2013
Abbreviated titleWHISPERS 2013
Country/TerritoryUnited States


  • Bayesian estimation
  • Gaussian process regression
  • Hyperspectral images
  • Nonlinear spectral unmixing
  • Unsupervised unmixing

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing


Dive into the research topics of 'Nonlinear hyperspectral unmixing using Gaussian processes'. Together they form a unique fingerprint.

Cite this