Fast Blind Hyperspectral Unmixing Based on Graph Laplacian

Jing Qin, Harlin Lee, Jocelyn T. Chi, Yifei Lou, Jocelyn Chanussot, Andrea L. Bertozzi

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

4 Scopus citations

Abstract

Blind hyperspectral unmixing is a challenging problem in remote sensing, which aims to infer material spectra and abundances from the given hyperspectral data. Many traditional methods suffer from poor identification of materials and/or expensive computational costs, which can be partially eased by trading the accuracy with efficiency. In this work, we propose a fast graph-based blind unmixing approach. In particular, we apply the Nyström method to efficiently approximate eigenvalues and eigenvectors of a matrix corresponding to a normalized graph Laplacian. Then the alternating direction method of multipliers (ADMM) yields a fast numerical algorithm. Experiments on a real dataset illustrate great potential of the proposed method in terms of accuracy and efficiency.

Original languageEnglish
Title of host publication2019 10th Workshop on Hyperspectral Imaging and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2019
ISBN (Electronic)9781728152943
DOIs
StatePublished - Sep 2019
Event10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 - Amsterdam, Netherlands
Duration: Sep 24 2019Sep 26 2019

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2019-September
ISSN (Print)2158-6276

Conference

Conference10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019
Country/TerritoryNetherlands
CityAmsterdam
Period9/24/199/26/19

Bibliographical note

Funding Information:
The initial research for this effort was conducted at the Research Collaboration Workshop for Women in Data Science and Mathematics that was held at ICERM, July 29-August 2, 2019. Funding for the workshop was provided by ICERM. In addition, Qin is supported by the NSF grant DMS- 1941197, Lou by the NSF CAREER grant DMS-1846690, Lee by the ONR grant N00014-19-1-2404, Chi by the NSF grant DMS-1760374, Chaussot by the Grant ANR-16 ASTR-0027-01, and Bertozzi by the DARPA grant FA8750-18-2-0066

Funding Information:
The initial research for this effort was conducted at the Research Collaboration Workshop for Women in Data Science and Mathematics that was held at ICERM, July 29-August 2, 2019. Funding for the workshop was provided by ICERM. In addition, Qin is supported by the NSF grant DMS-1941197, Lou by the NSF CAREER grant DMS-1846690, Lee by the ONR grant N00014-19-1-2404, Chi by the NSF grant DMS-1760374, Chaussot by the Grant ANR-16 ASTR-0027-01, and Bertozzi by the DARPA grant FA8750-18-2-0066

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Hyperspectral imaging
  • Nystrom method
  • alternating direction method of multipliers
  • graph Laplacian
  • hyperspectral unmixing

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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