Double reweighted sparse regression for hyperspectral unmixing

Rui Wang, Heng Chao Li, Wenzhi Liao, Aleksandra Pizurica

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

29 Scopus citations

Abstract

Spectral unmixing is an important technology in hyperspectral image applications. Recently, sparse regression is widely used in hyperspectral unmixing. This paper proposes a double reweighted sparse regression method for hyperspectral unmixing. The proposed method enhances the sparsity of abundance fraction in both spectral and spatial domains through double weights, in which one is used to enhance the sparsity of endmembers in the spectral library, and the other to improve the sparseness of abundance fraction of every material. Experimental results on both synthetic and real hyperspectral data sets demonstrate effectiveness of the proposed method both visually and quantitatively.

Original languageEnglish
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6986-6989
Number of pages4
ISBN (Electronic)9781509033324
DOIs
StatePublished - Nov 1 2016
Externally publishedYes
Event36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
Duration: Jul 10 2016Jul 15 2016

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2016-November

Conference

Conference36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/TerritoryChina
CityBeijing
Period07/10/1607/15/16

Keywords

  • Hyperspectral unmixing
  • double weights
  • sparse regression

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