Hyperspectral resolution enhancement using multisensor image data

J. Bieniarz, D. Cerra, X. X. Zhu, R. Muller, P. Reinartz

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

Abstract

In this paper we apply the Multi-Look Joint Sparsity Fusion algorithm to multisensor image data. Our algorithm at first performs sparse unmixing of the hyperspectral data and selects pixels for a second unmixing of the multispectral image. This is done by applying a joint sparsity model, which exploits similarities within neighbouring pixels. We test our resolution enhancement method using a hyperspectral and a multispectral image with a spatial resolution of 30 m and 3 m, respectively. To asses the results we evaluate the classification result of the resolution enhanced and original images.

Original languageEnglish
Title of host publication2015 7th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781467390156
DOIs
StatePublished - 2 Jul 2015
Externally publishedYes
Event7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015 - Tokyo, Japan
Duration: 2 Jun 20155 Jun 2015

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2015-June
ISSN (Print)2158-6276

Conference

Conference7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015
Country/TerritoryJapan
CityTokyo
Period2/06/155/06/15

Keywords

  • Resolution enhancement
  • hyperspectral image sharpening
  • joint sparsity
  • overcomplete spectral dictionary
  • spectral unmixing

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