Subspace-Based Bayesian Blind Source Separation for Hyperspectral Imagery

Abstract

In this paper, a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery is introduced. Following the linear mixing model, each pixel spectrum of the hyperspectral image is decomposed as a linear combination of pure endmember spectra. The estimation of the unknown endmember spectra and the corresponding abundances is conducted in a unified manner by generating the posterior distribution of the unknown parameters under a hierarchical Bayesian model. The proposed model accounts for nonnegativity and full-additivity constraints, and exploits the fact that the endmember spectra lie on a lower dimensional space. A Gibbs algorithm is proposed to generate samples distributed according to the posterior of interest. Simulation results illustrate the accuracy of the proposed joint Bayesian estimator.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2009
Accession Number
ADA513425

Entities

People

  • Alfred O. Hero
  • Jean-yves Tourneret
  • Martial Coulon
  • Nicolas Dobigeon
  • Said Moussaoui

Organizations

  • University of Toulouse (1896-1968)

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computational Science
  • Data Science
  • Dimensionality Reduction
  • Estimators
  • Gaussian Distributions
  • Hyperspectral Imagery
  • Information Science
  • Materials
  • Models
  • Monte Carlo Method
  • Sampling
  • Simulations
  • Spectra
  • Statistical Algorithms

Readers

  • Image Processing and Computer Vision.
  • Operations Research
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space