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Remote Sensing Images Inpainting based on Structured Low-Rank Matrix Approximation

  • Yue Hu
  • , Zidi Wei
  • , Kuangshi Zhao
  • Harbin Institute of Technology
  • CSIC Harbin No. 703 Research Institute

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

Abstract

Due to the sensor malfunction or poor observation conditions, optical remote sensing images often suffer from information loss such as dead pixels or cloud contamination. We propose a remote sensing image inpainting method based on structured low-rank matrix approximation. The hybrid regularizations are applied to recover the piecewise constant and the piecewise linear components of the image separately by exploiting the low-rank properties of the structured Toeplitz matrices of the two image components. The corresponding optimization problem can be solved using the half-circulant approximation of the Toeplitz matrix. Experimental results demonstrate the efficacy of the proposed method.

Original languageEnglish
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1341-1344
Number of pages4
ISBN (Electronic)9781728163741
DOIs
StatePublished - 26 Sep 2020
Event2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Duration: 26 Sep 20202 Oct 2020

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/TerritoryUnited States
CityVirtual, Waikoloa
Period26/09/202/10/20

Keywords

  • inpainting
  • remote sensing images
  • structured low-rank

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