Smooth spectral unmixing using total variation regularization and a first order roughness penalty

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

6 Citations (Scopus)

Abstract

Hyperspectral unmixing is the task of decomposing hyperspectral images into endmembers and their abundances. The endmembers are spectral signatures of specific material in the image and the abundances dictate the amount of the material found in each pixel. In this paper we present a blind signal separation method, based on the total variation penalty, that simultaneously estimates the endmembers and the abundances. We evaluate our method using both simulated and a real data set.

Original languageEnglish
Title of host publication2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
Pages2160-2163
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Melbourne, VIC, Australia
Duration: 21 Jul 201326 Jul 2013

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period21/07/1326/07/13

Other keywords

  • blind signal separation
  • cyclic descent
  • linear unmixing
  • majorization-minimization
  • roughness penalty
  • Spectral unmixing
  • total variation

Fingerprint

Dive into the research topics of 'Smooth spectral unmixing using total variation regularization and a first order roughness penalty'. Together they form a unique fingerprint.

Cite this