Canonical analysis of sentinel-1 radar and sentinel-2 optical data

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Abstract

This paper gives results from joint analyses of dual polarimety synthetic aperture radar data from the Sentinel-1 mission and optical data from the Sentinel-2 mission. The analyses are carried out by means of traditional canonical correlation analysis (CCA) and canonical information analysis (CIA). Where CCA is based on maximising correlation between linear combinations of the two data sets, CIA maximises mutual information between the two. CIA is a conceptually more pleasing method for the analysis of data with very different modalities such as radar and optical data. Although a little inconclusive as far as the change detection aspect is concerned, results show that CIA analysis gives conspicuously less noisy appearing images of canonical variates (CVs) than CCA. Also, the 2D histogram of the mutual information based leading CVs clearly reveals much more structure than the correlation based one. This gives promise for potentially better change detection results with CIA than can be obtained by means of CCA.
Original languageEnglish
Title of host publicationImage Analysis
Volume10270
PublisherIEEE
Publication date2017
Pages147-158
ISBN (Print)9783319591285
DOIs
Publication statusPublished - 2017
Event20th Scandinavian Conference on Image Analysis - Tromsø, Norway
Duration: 12 Jun 201714 Jun 2017

Conference

Conference20th Scandinavian Conference on Image Analysis
CountryNorway
CityTromsø
Period12/06/201714/06/2017
SeriesLecture Notes in Computer Science
Volume10270
ISSN0302-9743

Keywords

  • Theoretical Computer Science
  • Computer Science (all)
  • Canonical correlation analysis
  • Canonical information analysis
  • Correlation methods
  • Information analysis
  • Radar
  • Synthetic aperture radar
  • 2-D histograms
  • Analysis of data
  • Canonical analysis
  • Change detection
  • Joint analysis
  • Linear combinations
  • Mutual informations
  • Image analysis

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