Graph-regularized coupled spectral unmixing for multisensor time-series analysis

Naoto Yokoya, Xiao Xiang Zhu, Antonio Plaza

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

Abstract

A new methodology that solves unmixing problems involving a set of multisensor time-series spectral images is proposed in order to understand dynamic changes of the surface at a subpixel scale. The proposed methodology couples multiple unmixing problems via regularization on graphs between the multisensor time-series data to obtain robust and stable unmixing solutions beyond data modalities owing to different sensor characteristics and the effects of non-optimal atmospheric correction. A synthetic dataset that includes seasonal and trend changes on the surface and the residuals of non-optimal atmospheric correction is used for numerical validation. Experimental results demonstrate the effectiveness of the proposed methodology.

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 - 28 Jun 2016
Event8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016 - Los Angeles, United States
Duration: 21 Aug 201624 Aug 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
Period21/08/1624/08/16

Keywords

  • Change detection
  • Coupled spectral unmixing
  • Multisensor data fusion
  • Time-series analysis

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