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dc.contributor.authorYe, Minchao
dc.contributor.authorQian, Yuntao
dc.contributor.authorZhou, Jun
dc.date.accessioned2017-05-03T16:11:37Z
dc.date.available2017-05-03T16:11:37Z
dc.date.issued2015
dc.identifier.issn0196-2892
dc.identifier.doi10.1109/TGRS.2014.2363101
dc.identifier.urihttp://hdl.handle.net/10072/69211
dc.description.abstractHyperspectral imagery (HSI) denoising is a challenging problem because of the difficulty in preserving both spectral and spatial structures simultaneously. In recent years, sparse coding, among many methods dedicated to the problem, has attracted much attention and showed state-of-the-art performance. Due to the low-rank property of natural images, an assumption can be made that the latent clean signal is a linear combination of a minority of basis atoms in a dictionary, while the noise component is not. Based on this assumption, denoising can be explored as a sparse signal recovery task with the support of a dictionary. In this paper, we propose to solve the HSI denoising problem by sparse nonnegative matrix factorization (SNMF), which is an integrated model that combines parts-based dictionary learning and sparse coding. The noisy image is used as the training data to learn a dictionary, and sparse coding is used to recover the image based on this dictionary. Unlike most HSI denoising approaches, which treat each band image separately, we take the joint spectral-spatial structure of HSI into account. Inspired by multitask learning, a multitask SNMF (MTSNMF) method is developed, in which bandwise denoising is linked across the spectral domain by sharing a common coefficient matrix. The intrinsic image structures are treated differently but interdependently within the spatial and spectral domains, which allows the physical properties of the image in both spatial and spectral domains to be reflected in the denoising model. The experimental results show that MTSNMF has superior performance on both synthetic and real-world data compared with several other denoising methods.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent2085791 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom2621
dc.relation.ispartofpageto2639
dc.relation.ispartofissue5
dc.relation.ispartofjournalIEEE Transactions on Geoscience and Remote Sensing
dc.relation.ispartofvolume53
dc.rights.retentionY
dc.subject.fieldofresearchGeophysics
dc.subject.fieldofresearchImage processing
dc.subject.fieldofresearchGeomatic engineering
dc.subject.fieldofresearchcode3706
dc.subject.fieldofresearchcode460306
dc.subject.fieldofresearchcode4013
dc.titleMulti-task sparse nonnegative matrix factorization for joint spectral-spatial hyperspectral imagery denoising
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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gro.griffith.authorZhou, Jun


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