Nonlinear spectral unmixing of hyperspectral images using residual component analysis

Yoann Altmann*, Steve McLaughlin

*Corresponding author for this work

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


This paper presents a nonlinear mixing model for linear/nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

Original languageEnglish
Title of host publication2014 Sensor Signal Processing for Defence
Number of pages5
ISBN (Print)978-1-4799-5294-6
Publication statusPublished - 2014
Event4th Sensor Signal Processing for Defence 2014 - Edinburgh, Edinburgh, United Kingdom
Duration: 8 Sept 20149 Sept 2014


Conference4th Sensor Signal Processing for Defence 2014
Abbreviated titleSSPD 2014
Country/TerritoryUnited Kingdom


  • Hyperspectral imagery
  • nonlinear spectral unmixing
  • residual component analysis
  • nonlinearity detection


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