Nonlocal similarity regularization for sparse hyperspectral unmixing

Rui Wang, Heng Chao Li

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

4 Scopus citations

Abstract

This paper is concerned with semisupervised hyperspectral unmixing using a nonlocal similarity prior on the abundance images. To this end, the nonlocal self-similarity regularization is incorporated into the classical sparse regression formula to propose a new model for hyperspectral sparse unmixing. The rationale is the idea that there are many nonlocal similar patches to the given patch in the abundance images. The effectiveness of the proposed algorithm is illustrated using the synthetic and real data sets.

Original languageEnglish
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2926-2929
Number of pages4
ISBN (Electronic)9781479957750
DOIs
StatePublished - Nov 4 2014
Externally publishedYes
EventJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada
Duration: Jul 13 2014Jul 18 2014

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

ConferenceJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Country/TerritoryCanada
CityQuebec City
Period07/13/1407/18/14

Keywords

  • Hyperspectral remote sensing
  • nonlocal similarity regularization
  • sparse unmixing
  • spectral library

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