Hyperspectral Unmixing Via Plug-And-Play Priors

Xiuheng Wang, Min Zhao, Jie Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Hyperspectral unmixing aims at separating a mixed pixel into a set of pure spectral signatures and their corresponding fractional abundances. Investigating prior spatial and spectral information to regularize the unmixing problem can effectively improve the estimation performance. However, handcrafting a powerful regularizer is a non-trivial task and complex regularizers introduce extra difficulties in solving the optimization problem. In this paper, we present a flexible spectral unmixing method using plug-and-play priors. This method benefits from the alternating direction method of multipliers (ADMM) to decompose the optimization problem into iterative subproblems and incorporates the image denoisers as prior models in a subproblem. In this form, we can plug in various image denoising operations to bypass handcrafting regularizers. We demonstrate the superiority of the proposed unmixing method comparing with other state-of-the-art methods both on synthetic data and real airborne data.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages1063-1067
Number of pages5
ISBN (Electronic)9781728163956
DOIs
StatePublished - Oct 2020
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: 25 Sep 202028 Sep 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period25/09/2028/09/20

Keywords

  • ADMM
  • Hyperspectral unmixing
  • image denoising
  • plug-and-play
  • prior modeling

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