Bilinear models for nonlinear unmixing of hyperspectral images

Yoann Altmann*, Nicolas Dobigeon, Jean-Yves Tourneret

*Corresponding author for this work

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

40 Citations (Scopus)

Abstract

This paper compares several nonlinear models recently introduced for hyperspectral image unmixing. All these models consist of bilinear models that have shown interesting properties for hyperspectral images subjected to multipath effects. The first part of this paper presents different algorithms allowing the parameters of these models to be estimated. The relevance and flexibility of these models for spectral unmixing are then investigated by comparing the reconstruction errors and spectral angle mappers computed from synthetic and real dataset. This kind of study is important to determine which mixture model should be used in practical applications for hyper-spectral image unmixing.

Original languageEnglish
Title of host publication2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
PublisherIEEE
ISBN (Electronic)9781457722011
ISBN (Print)9781457722028
DOIs
Publication statusPublished - 18 Nov 2011
Event3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing 2011 - Lisbon, Portugal
Duration: 6 Jun 20119 Jun 2011

Conference

Conference3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing 2011
Abbreviated titleWHISPERS 2011
Country/TerritoryPortugal
CityLisbon
Period6/06/119/06/11

Keywords

  • Hyperspectral imagery
  • linear model
  • nonlinear model
  • unmixing

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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