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Non-local tensor completion for multitemporal remotely sensed images inpainting

Ji, Teng-yu and Yokoya, Naoto and Zhu, Xiao Xiang and Huang, Ting-zhu (2018) Non-local tensor completion for multitemporal remotely sensed images inpainting. IEEE Transactions on Geoscience and Remote Sensing, 56 (6), pp. 3047-3061. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2018.2790262 ISSN 0196-2892

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Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8303784

Abstract

Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. The paper aims at reconstructing the missing information by a non-local low-rank tensor completion method (NL-LRTC). First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then low-rankness of the identified 4-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared to other patch based methods such as the recent proposed PM-MTGSR method.

Item URL in elib:https://elib.dlr.de/120440/
Document Type:Article
Title:Non-local tensor completion for multitemporal remotely sensed images inpainting
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ji, Teng-yuUniversity of Electronic Science and Technology of ChinaUNSPECIFIED
Yokoya, NaotoNaoto.Yokoya (at) dlr.deUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Huang, Ting-zhuUniversity of Electronic Science and Technology of ChinaUNSPECIFIED
Date:January 2018
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:56
DOI :10.1109/TGRS.2018.2790262
Page Range:pp. 3047-3061
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Multitemporal remotely sensed images, missing information reconstruction, tensor completion
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Wang, Yuanyuan
Deposited On:19 Jun 2018 12:14
Last Modified:23 Feb 2019 00:22

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