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Article: An Adaptive Weighted Tensor Completion Method for the Recovery of Remote Sensing Images with Missing Data

TitleAn Adaptive Weighted Tensor Completion Method for the Recovery of Remote Sensing Images with Missing Data
Authors
Keywordsmissing data reconstruction
Adaptive weighted
tensor completion
remote sensing
Issue Date2017
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2017, v. 55, n. 6, p. 3367-3381 How to Cite?
Abstract© 2017 IEEE. Missing information, such as dead pixel values and cloud effects, is very common image quality degradation problems in remote sensing. Missing information can reduce the accuracy of the subsequent image processing, in applications such as classification, unmixing, and target detection, and even the quantitative retrieval process. The main aim of this paper is to study an adaptive weighted tensor completion (AWTC) method for the recovery of remote sensing images with missing data. Our idea is to collectively make use of the spatial, spectral, and temporal information to build a new weighted tensor low-rank regularization model for recovering the missing data. In the model, the weights are determined adaptively by considering the contribution of the spatial, spectral, and temporal information in each dimension. Experimental results based on both simulated and real data sets are presented to verify that the proposed method can recover missing data, and its performance is found to be better than the other tested methods. In the simulated experiments, the peak signal-to-noise ratio is improved by more than 3 dB, compared with the original tensor completion model. In the real data experiments, the proposed AWTC model can better recover the dead line problem in Aqua Moderate Resolution Imaging Spectroradiometer band 6 and the scan-line corrector-off problem in enhanced thematic mapper plus images, with the smallest spectral distortion.
Persistent Identifierhttp://hdl.handle.net/10722/277065
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorNg, Michael Kwok Po-
dc.contributor.authorYuan, Qiangqiang-
dc.contributor.authorYan, Li-
dc.contributor.authorSun, Jing-
dc.date.accessioned2019-09-18T08:35:30Z-
dc.date.available2019-09-18T08:35:30Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2017, v. 55, n. 6, p. 3367-3381-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/277065-
dc.description.abstract© 2017 IEEE. Missing information, such as dead pixel values and cloud effects, is very common image quality degradation problems in remote sensing. Missing information can reduce the accuracy of the subsequent image processing, in applications such as classification, unmixing, and target detection, and even the quantitative retrieval process. The main aim of this paper is to study an adaptive weighted tensor completion (AWTC) method for the recovery of remote sensing images with missing data. Our idea is to collectively make use of the spatial, spectral, and temporal information to build a new weighted tensor low-rank regularization model for recovering the missing data. In the model, the weights are determined adaptively by considering the contribution of the spatial, spectral, and temporal information in each dimension. Experimental results based on both simulated and real data sets are presented to verify that the proposed method can recover missing data, and its performance is found to be better than the other tested methods. In the simulated experiments, the peak signal-to-noise ratio is improved by more than 3 dB, compared with the original tensor completion model. In the real data experiments, the proposed AWTC model can better recover the dead line problem in Aqua Moderate Resolution Imaging Spectroradiometer band 6 and the scan-line corrector-off problem in enhanced thematic mapper plus images, with the smallest spectral distortion.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectmissing data reconstruction-
dc.subjectAdaptive weighted-
dc.subjecttensor completion-
dc.subjectremote sensing-
dc.titleAn Adaptive Weighted Tensor Completion Method for the Recovery of Remote Sensing Images with Missing Data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2017.2670021-
dc.identifier.scopuseid_2-s2.0-85015706955-
dc.identifier.volume55-
dc.identifier.issue6-
dc.identifier.spage3367-
dc.identifier.epage3381-
dc.identifier.isiWOS:000402063500024-
dc.identifier.issnl0196-2892-

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