Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes

Yoann Altmann, Nicolas Dobigeon, Steve McLaughlin, Jean-Yves Tourneret

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

Abstract

This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation procedure is applied simultaneously to all pixels of the image by maximizing an appropriate posterior distribution which does not depend on the endmembers. After determining the abundances of all image pixels, the endmembers contained in the image are estimated by using Gaussian process regression. The performance of the resulting unsupervised unmixing strategy is evaluated through simulations conducted on synthetic data.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages1249-1252
Number of pages4
ISBN (Print)9781467300469
DOIs
Publication statusPublished - 31 Aug 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2012
CountryJapan
CityKyoto
Period25/03/1230/03/12

Keywords

  • Gaussian Processes
  • hyperspectral images
  • Nonlinear unmixing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Altmann, Y., Dobigeon, N., McLaughlin, S., & Tourneret, J-Y. (2012). Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1249-1252). IEEE. https://doi.org/10.1109/ICASSP.2012.6288115