Missing Data Reconstruction for Remote Sensing Images with Weighted Low-Rank Tensor Model

Qing Cheng, Qiangqiang Yuan*, Kwok Po NG, Huanfeng Shen, Liangpei Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Missing data reconstruction for remote sensing images, such as dead-pixel recovery and cloud removal, is important for remote sensing data applications. Missing information reconstruction is well known as being an ill-posed inverse problem. In this paper, a weighted low-rank tensor regularization model is proposed to handle the problem. The proposed model fully utilizes the correlations in the spatial, spectral, and temporal components of the remote sensing images to adaptively deal with the varied missing data problems, including the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) band 6 dead line problem, the Landsat scan-line corrector failure (SLC-off) problem, and cloud contamination. A double-weighted treatment is developed to balance the contributions from the different dimensions and preserve the different structures and textures in remote sensing images. The experiments undertaken confirmed the good performance of the proposed method, and the reconstruction results of the proposed method, in both visual effect and quantitative evaluation, were superior to those of the other methods.

Original languageEnglish
Article number8852746
Pages (from-to)142339-142352
Number of pages14
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Scopus Subject Areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

User-Defined Keywords

  • low-rank model
  • Missing information reconstruction
  • remote sensing
  • tensor completion

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