Hyperspectral unmixing by reweighted low rank and total variation

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

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

10 Scopus citations

Abstract

In recent years, sparse regression has drawn much attention in hyperspectral unmixing. The well known sparse unmixing via variable splitting augmented Lagrangian (SUnSAL) and sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAl-TV) aim to find the sparsest abundance of every data vector individually. However, these methods ignore the global structure of all the vectors. In this paper, we propose a novel hyperspectral unmixing method by exploiting low rank property of the abundance matrix. Our proposed method find the lowest-rank representation of a collection of the abundance vectors by using reweighted low rank constraint. This way, our proposed unmixing method better captures the global structure of the abundance matrix and improve the accuracy of abundance estimation. Our approach also takes the spatial context into account by a TV constraint. Experimental results on both the synthetic and real hyperspectral data demonstrate the effectiveness of our proposed algorithm.

Original languageEnglish
Title of host publication2016 8th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781509006083
DOIs
StatePublished - Jun 28 2016
Externally publishedYes
Event8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016 - Los Angeles, United States
Duration: Aug 21 2016Aug 24 2016

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume0
ISSN (Print)2158-6276

Conference

Conference8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016
Country/TerritoryUnited States
CityLos Angeles
Period08/21/1608/24/16

Keywords

  • Hyperspectral remote sensing
  • Low rank
  • Reweighted
  • Unmixing

Fingerprint

Dive into the research topics of 'Hyperspectral unmixing by reweighted low rank and total variation'. Together they form a unique fingerprint.

Cite this