Compressed sensing for phase unwrapping of interferometric SAR data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We approach to the problem of wave-front reconstruction via phase unwrapping of interferograms observed by interferometric synthetic aperture radar (SAR), from the viewpoints of Bayesian statistical inference and compressed sensing. For this purpose, we apply sparse representation for compressed sensing to the Bayesian wave-front reconstruction model from SAR interferograms by Saika and Uezu [1]. In the formulation of the problem taking sparse representation into account, the MAP estimate is found to lead to a phase unwrapping algorithm which can be interpreted as a quadratic programming problem. Numerical experiments on an artificial wave-front make it clear that the algorithm effectively removes noise to reconstruct the wave-front, although it suffers from the errors similar to block noise in image processing.

Original languageEnglish
Title of host publicationICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings
PublisherIEEE Computer Society
Pages989-993
Number of pages5
Volume2017-October
ISBN (Electronic)9788993215137
DOIs
Publication statusPublished - Dec 13 2017
Event17th International Conference on Control, Automation and Systems, ICCAS 2017 - Jeju, Korea, Republic of
Duration: Oct 18 2017Oct 21 2017

Other

Other17th International Conference on Control, Automation and Systems, ICCAS 2017
CountryKorea, Republic of
CityJeju
Period10/18/1710/21/17

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Keywords

  • Bayesian inference
  • Compressed sensing
  • Phase unwrapping
  • Remote sensing
  • SAR

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Aida, T. (2017). Compressed sensing for phase unwrapping of interferometric SAR data. In ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings (Vol. 2017-October, pp. 989-993). IEEE Computer Society. https://doi.org/10.23919/ICCAS.2017.8204366