Miss data reconstruction in remote sensing images with a double weighted tensor low rank model

Qiangqiang Yuan, Kwok Po NG, Huanfeng Shen, Liangpei Zhang, Jie Li

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Missing data reconstruction (e.g., dead pixel repair and cloud removing) in remote sensing images is a very important problem for the subsequent image analysis. It is well-known that missing data reconstruction is an ill-posed problem. In remote sensing images, there is a strong correlation in spectral frequencies or in temporal frames, and also there are a lot of self-similarity patterns in spatial domain. We can make use of these properties to derive low rank matrices according to their spectral, temporal and spatial dimensions. In this paper, we propose a tensor completion model based on these low rank matrices to deal with missing data reconstruction problem. We also present a weighting method for spectral, temporal and spatial dimensions and for their distribution of singular values. Our experimental results demonstrate that the weighting method can recover remote images very well. In particular, we show the effectiveness of the proposed method for both simulated and real data sets, and the performance of the proposed in terms of visual and quantitative measures is better than those of the other testing methods.

Original languageEnglish
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4036-4039
Number of pages4
ISBN (Electronic)9781509049516
DOIs
Publication statusPublished - 1 Dec 2017
Event37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States
Duration: 23 Jul 201728 Jul 2017

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2017-July

Conference

Conference37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Country/TerritoryUnited States
CityFort Worth
Period23/07/1728/07/17

Scopus Subject Areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

User-Defined Keywords

  • Missing data reconstruction
  • Remote sensing
  • Tensor low rank model

Fingerprint

Dive into the research topics of 'Miss data reconstruction in remote sensing images with a double weighted tensor low rank model'. Together they form a unique fingerprint.

Cite this