A comparison of nonlinear mixing models for vegetated areas using simulated and real hyperspectral data

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

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Spectral unmixing (SU) is a crucial processing step when analyzing hyperspectral data. In such analysis, most of the work in the literature relies on the widely acknowledged linear mixing model to describe the observed pixels. Unfortunately, this model has been shown to be of limited interest for specific scenes, in particular when acquired over vegetated areas. Consequently, in the past few years, several nonlinear mixing models have been introduced to take nonlinear effects into account while performing SU. These models have been proposed empirically, however, without any thorough validation. In this paper, the authors take advantage of two sets of real and physical-based simulated data to validate the accuracy of various nonlinear models in vegetated areas. These physics-based models, and their corresponding unmixing algorithms, are evaluated with respect to their ability of fitting the measured spectra and providing an accurate estimation of the abundance coefficients, considered as the spatial distribution of the materials in each pixel.

Original languageEnglish
Pages (from-to)1869-1878
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume7
Issue number6
DOIs
Publication statusPublished - Jun 2014

Keywords

  • Hyperspectral imagery
  • nonlinear spectral mixtures
  • ray tracing
  • spectral unmixing (SU)
  • vegetated areas

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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