Robust linear spectral unmixing using outlier detection

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

3 Citations (Scopus)


This paper presents a Bayesian algorithm for linear spectral unmixing that accounts for outliers present in the data. The proposed model assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional term modelling outliers and additive Gaussian noise. A Markov random field is considered for outlier detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and outlier detection algorithm. Simulations conducted with synthetic data demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.

Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Number of pages5
ISBN (Print)9781467369978
Publication statusPublished - 4 Aug 2015
Event40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015 - Brisbane, Australia
Duration: 19 Apr 201524 Apr 2015


Conference40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015
Abbreviated titleICASSP 2015


  • Bayesian estimation
  • Hyperspectral imagery
  • MCMC
  • nonlinearity detection
  • unsupervised spectral unmixing

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering


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