Fast hyperspectral unmixing in presence of sparse multiple scattering nonlinearities

Abderrahim Halimi, Jose Bioucas-Dias, Nicolas Dobigeon, Gerald S. Buller, Steve McLaughlin

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

Abstract

This paper presents a novel nonlinear hyperspectral mixture model and its associated supervised unmixing algorithm. The model assumes a linear mixing model corrupted by an additive term which accounts for multiple scattering nonlinearities (NL). The proposed model generalizes bilinear models by taking into account higher order interaction terms. The inference of the abundances and nonlinearity coefficients of this model is formulated as a convex optimization problem suitable for fast estimation algorithms. This formulation accounts for constraints such as the sum-to-one and nonnegativity of the abundances, the non-negativity of the nonlinearity coefficients, and the spatial sparseness of the residuals. The resulting convex problem is solved using the alternating direction method of multipliers (ADMM) whose convergence is ensured theoretically. The proposed mixture model and its unmixing algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inference and the computational complexity when compared to the state-of-the-art algorithms.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PublisherIEEE
Pages3111-3115
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 19 Jun 2017
Event42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameIEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE
ISSN (Electronic)2379-190X

Conference

Conference42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017
Abbreviated titleICASSP 2017
CountryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • ADMM
  • collaborative sparse regression
  • convex optimization
  • Hyperspectral imagery
  • nonlinear unmixing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Fast hyperspectral unmixing in presence of sparse multiple scattering nonlinearities'. Together they form a unique fingerprint.

  • Cite this

    Halimi, A., Bioucas-Dias, J., Dobigeon, N., Buller, G. S., & McLaughlin, S. (2017). Fast hyperspectral unmixing in presence of sparse multiple scattering nonlinearities. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 3111-3115). (IEEE International Conference on Acoustics, Speech, and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP.2017.7952729