Efficient and automated multimodal satellite data registration through MRFs and linear programming

Konstantinos Karantzalos, Aristeidis Sotiras, Nikos Paragios

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

19 Scopus citations

Abstract

The accurate and automated registration of multimodal remote sensing data is of fundamental importance for numerous emerging geospatial environmental and engineering applications. However, the registration of very large multimodal, multitemporal, with different spatial resolutions data is, still, an open matter. To this end, we propose a generic and automated registration framework based on Markov Random Fields (MRFs) and efficient linear programming. The discrete optimization setting along with the introduced data-specific energy terms form a modular approach with respect to the similarity criterion allowing to fully exploit the spectral properties of multimodal remote sensing datasets. The proposed approach was validated both qualitatively and quantitatively demonstrating its potentials on very large (more than 100M pixels) multitemporal remote sensing datasets. In particular, in terms of spatial accuracy the geometry of the optical and radar data has been recovered with displacement errors of less than 2 and 3 pixels, respectively. In terms of computational efficiency the optical data term can converge after 7-8 minutes, while the radar data term after less than 15 minutes.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014
PublisherIEEE Computer Society
Pages335-342
Number of pages8
ISBN (Electronic)9781479943098, 9781479943098
DOIs
StatePublished - Sep 24 2014
Event2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014 - Columbus, United States
Duration: Jun 23 2014Jun 28 2014

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014
Country/TerritoryUnited States
CityColumbus
Period06/23/1406/28/14

Keywords

  • Alignment
  • Image
  • Markov Random Fields
  • Multisensor
  • Multitemporal
  • Radar
  • Remote Sensing

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