Hyperspectral unmixing by reweighted low rank and total variation

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

Research output: Contribution to conferencePaper

3 Citations (Scopus)

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
Number of pages4
DOIs
Publication statusPublished - 19 Oct 2017
Event2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Los Angeles, United States
Duration: 21 Aug 201624 Aug 2016

Workshop

Workshop2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Abbreviated titleWHISPERS 2016
CountryUnited States
CityLos Angeles
Period21/08/1624/08/16

Keywords

  • unmixing
  • hyperspectral remote sensing
  • reweighted
  • low rank
  • hyperspectral Imaging
  • geophysical image processing
  • regression analysis
  • optimization

Cite this

Wang, R., Liao, W., Li, H-C., Zhang, H., & Pizurica, A. (2017). Hyperspectral unmixing by reweighted low rank and total variation. Paper presented at 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, United States. https://doi.org/10.1109/WHISPERS.2016.8071668