Sparse Spectral Unmixing of Hyperspectral Images using Expectation-Propagation

Zeng Li, Yoann Altmann, Jie Chen, Stephen McLaughlin, Susanto Rahardja

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

Abstract

The aim of spectral unmixing of hyperspectral images is to determine the component materials and their associated abundances from mixed pixels. In this paper, we present sparse linear unmixing via an Expectation-Propagation method based on the classical linear mixing model and a spike-and-slab prior promoting abundance sparsity. The proposed method, which allows approximate uncertainty quantification (UQ), is compared to existing sparse unmixing methods, including Monte Carlo strategies traditionally considered for UQ. Experimental results on synthetic data and real hyperspectral data illustrate the benefits of the proposed algorithm over state-of-art linear unmixing methods.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020
PublisherIEEE
Pages197-200
Number of pages4
ISBN (Electronic)978-1-7281-8067-0, 978-1-7281-8068-7
DOIs
Publication statusPublished - 29 Dec 2020
Event2020 IEEE International Conference on Visual Communications and Image Processing - Virtual, Macau, China
Duration: 1 Dec 20204 Dec 2020

Conference

Conference2020 IEEE International Conference on Visual Communications and Image Processing
Abbreviated titleVCIP 2020
CountryChina
CityVirtual, Macau
Period1/12/204/12/20

Keywords

  • Approximate Bayesian method
  • Expectation-Propagation
  • Spectral unmixing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Media Technology

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