Nonlinear unmixing of vegetated areas: A model comparison based on simulated and real hyperspectral data

Nicolas Dobigeon, Laurent Tits, Ben Somers, Yoann Altmann, Pol Coppin

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

2 Citations (Scopus)

Abstract

When analyzing remote sensing hyperspectral images, numerous works dealing with spectral unmixing assume the pixels result from linear combinations of the endmember signatures. However, this assumption cannot be fulfilled, in particular when considering images acquired over vegetated areas. As a consequence, several nonlinear mixing models have been recently derived to take various nonlinear effects into account when unmixing hyperspectral data. Unfortunately, these models have been empirically proposed and without thorough validation. This paper attempts to fill this gap by taking advantage of two sets of real and physical-based simulated data. The accuracy of various linear and nonlinear models and the corresponding unmixing algorithms is evaluated with respect to their ability of fitting the sensed pixels and of providing accurate estimates of the abundances.

Original languageEnglish
Title of host publication2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
PublisherIEEE
ISBN (Electronic)9781467390125
DOIs
Publication statusPublished - 26 Oct 2017
Event6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing 2014 - Lausanne, Switzerland
Duration: 24 Jun 201427 Jun 2014

Publication series

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

Conference

Conference6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing 2014
Abbreviated titleWHISPERS 2014
CountrySwitzerland
CityLausanne
Period24/06/1427/06/14

Keywords

  • Hyperspectral imagery
  • nonlinear spectral mixtures
  • ray tracing
  • spectral unmixing
  • vegetated areas

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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