Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares

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

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

18 Citations (Scopus)

Abstract

This paper studies a linear radial basis function network (RBFN) for unmixing hyperspectral images. The proposed RBFN assumes that the observed pixel reflectances are nonlinear mixtures of known end-members (extracted from a spectral library or estimated with an end-member extraction algorithm), with unknown proportions (usually referred to as abundances). We propose to estimate the model abundances using a linear combination of radial basis functions whose weights are estimated using training samples. The main contribution of this paper is to study an orthogonal least squares algorithm which allows the number of RBFN centers involved in the abundance estimation to be significantly reduced. The resulting abundance estimator is combined with a fully constrained estimation procedure ensuring positivity and sum-to-one constraints for the abundances. The performance of the nonlinear unmixing strategy is evaluated with simulations conducted on synthetic and real data.

Original languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE
Pages1151-1154
Number of pages4
ISBN (Electronic)9781457710056
DOIs
Publication statusPublished - 20 Oct 2011
Event2011 IEEE International Geoscience and Remote Sensing Symposium - Vancouver, BC, Canada
Duration: 24 Jul 201129 Jul 2011

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
ISSN (Print)2153-6996
ISSN (Electronic)2153-7003

Conference

Conference2011 IEEE International Geoscience and Remote Sensing Symposium
Abbreviated titleIGARSS 2011
CountryCanada
CityVancouver, BC
Period24/07/1129/07/11

Keywords

  • hyperspectral image
  • Radial basis functions
  • spectral unmixing

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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  • Cite this

    Altmann, Y., Dobigeon, N., Tourneret, J-Y., & McLaughlin, S. (2011). Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares. In 2011 IEEE International Geoscience and Remote Sensing Symposium (pp. 1151-1154). (International Geoscience and Remote Sensing Symposium (IGARSS)). IEEE. https://doi.org/10.1109/IGARSS.2011.6049401