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Article: Coupled segmentation and denoising/deblurring models for hyperspectral material identification

TitleCoupled segmentation and denoising/deblurring models for hyperspectral material identification
Authors
KeywordsSegmentation
Tensors
Deblurring
Compressive representation
Denoising
Hyperspectral image analysis
Issue Date2012
Citation
Numerical Linear Algebra with Applications, 2012, v. 19, n. 1, p. 153-173 How to Cite?
AbstractA crucial aspect of spectral image analysis is the identification of the materials present in the object or scene being imaged and to quantify their abundance in the mixture. An increasingly useful approach to extracting such underlying structure is to employ image classification and object identification techniques to compressively represent the original data cubes by a set of spatially orthogonal bases and a set of spectral signatures. Owing to the increasing quantity of data usually encountered in hyperspectral data sets, effective data compressive representation is an important consideration, and noise and blur can present data analysis problems. In this paper, we develop image segmentation methods for hyperspectral space object material identification. We also couple the segmentation with a hyperspectral image data denoising/deblurring model and propose this method as an alternative to a tensor factorization methods proposed recently for space object material identification. The model provides the segmentation result and the restored image simultaneously. Numerical results show the effectiveness of our proposed combined model in hyperspectral material identification. © 2010 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/276913
ISSN
2023 Impact Factor: 1.8
2023 SCImago Journal Rankings: 0.932
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Fang-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorPlemmons, Robert J.-
dc.date.accessioned2019-09-18T08:35:02Z-
dc.date.available2019-09-18T08:35:02Z-
dc.date.issued2012-
dc.identifier.citationNumerical Linear Algebra with Applications, 2012, v. 19, n. 1, p. 153-173-
dc.identifier.issn1070-5325-
dc.identifier.urihttp://hdl.handle.net/10722/276913-
dc.description.abstractA crucial aspect of spectral image analysis is the identification of the materials present in the object or scene being imaged and to quantify their abundance in the mixture. An increasingly useful approach to extracting such underlying structure is to employ image classification and object identification techniques to compressively represent the original data cubes by a set of spatially orthogonal bases and a set of spectral signatures. Owing to the increasing quantity of data usually encountered in hyperspectral data sets, effective data compressive representation is an important consideration, and noise and blur can present data analysis problems. In this paper, we develop image segmentation methods for hyperspectral space object material identification. We also couple the segmentation with a hyperspectral image data denoising/deblurring model and propose this method as an alternative to a tensor factorization methods proposed recently for space object material identification. The model provides the segmentation result and the restored image simultaneously. Numerical results show the effectiveness of our proposed combined model in hyperspectral material identification. © 2010 John Wiley & Sons, Ltd.-
dc.languageeng-
dc.relation.ispartofNumerical Linear Algebra with Applications-
dc.subjectSegmentation-
dc.subjectTensors-
dc.subjectDeblurring-
dc.subjectCompressive representation-
dc.subjectDenoising-
dc.subjectHyperspectral image analysis-
dc.titleCoupled segmentation and denoising/deblurring models for hyperspectral material identification-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/nla.750-
dc.identifier.scopuseid_2-s2.0-84855242028-
dc.identifier.volume19-
dc.identifier.issue1-
dc.identifier.spage153-
dc.identifier.epage173-
dc.identifier.eissn1099-1506-
dc.identifier.isiWOS:000298595300011-
dc.identifier.issnl1070-5325-

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