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Article: An Error-Bound-Regularized Sparse Coding for Spatiotemporal Reflectance Fusion

TitleAn Error-Bound-Regularized Sparse Coding for Spatiotemporal Reflectance Fusion
Authors
KeywordsDictionary perturbation
error bound regularization
multitemporal image
sparse representation
spatiotemporal reflectance fusion (SPTM)
Issue Date2015
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2015, v. 53, n. 12, p. 6791-6803 How to Cite?
AbstractThis paper attempts to demonstrate that addressing the dictionary perturbations and the individual representation of the coupled images can generally result in positive effects with respect to sparse-representation-based spatiotemporal reflectance fusion (SPTM). We propose to adapt the dictionary perturbations with an error-bound-regularized method and formulate the dictionary perturbations to be a sparse elastic net regression problem. Moreover, we also utilize semi-coupled dictionary learning (SCDL) to address the differences between the high-spatial-resolution and low-spatial-resolution images, and we propose the error-bound-regularized SCDL (EBSCDL) model by also imposing an error bound regularization. Two data sets of Landsat Enhanced Thematic Mapper Plus data and Moderate Resolution Imaging Spectroradiometer acquisitions were used to validate the proposed models. The spatial and temporal adaptive reflectance fusion model and the original SPTM were also implemented and compared. The experimental results consistently show the positive effect of the proposed methods for SPTM, with smaller differences in scatter plot distribution and higher peak-signal-to-noise ratio and structural similarity index measures.
Persistent Identifierhttp://hdl.handle.net/10722/329463
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, Bo-
dc.contributor.authorHuang, Bo-
dc.contributor.authorZhang, Liangpei-
dc.date.accessioned2023-08-09T03:32:58Z-
dc.date.available2023-08-09T03:32:58Z-
dc.date.issued2015-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2015, v. 53, n. 12, p. 6791-6803-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/329463-
dc.description.abstractThis paper attempts to demonstrate that addressing the dictionary perturbations and the individual representation of the coupled images can generally result in positive effects with respect to sparse-representation-based spatiotemporal reflectance fusion (SPTM). We propose to adapt the dictionary perturbations with an error-bound-regularized method and formulate the dictionary perturbations to be a sparse elastic net regression problem. Moreover, we also utilize semi-coupled dictionary learning (SCDL) to address the differences between the high-spatial-resolution and low-spatial-resolution images, and we propose the error-bound-regularized SCDL (EBSCDL) model by also imposing an error bound regularization. Two data sets of Landsat Enhanced Thematic Mapper Plus data and Moderate Resolution Imaging Spectroradiometer acquisitions were used to validate the proposed models. The spatial and temporal adaptive reflectance fusion model and the original SPTM were also implemented and compared. The experimental results consistently show the positive effect of the proposed methods for SPTM, with smaller differences in scatter plot distribution and higher peak-signal-to-noise ratio and structural similarity index measures.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectDictionary perturbation-
dc.subjecterror bound regularization-
dc.subjectmultitemporal image-
dc.subjectsparse representation-
dc.subjectspatiotemporal reflectance fusion (SPTM)-
dc.titleAn Error-Bound-Regularized Sparse Coding for Spatiotemporal Reflectance Fusion-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2015.2448100-
dc.identifier.scopuseid_2-s2.0-85027943778-
dc.identifier.volume53-
dc.identifier.issue12-
dc.identifier.spage6791-
dc.identifier.epage6803-
dc.identifier.isiWOS:000361532400038-

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