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dc.contributor.authorShkvarko, Yuriy
dc.contributor.authorVillalón-Turrubiates, Iván E.
dc.date.accessioned2016-04-05T19:10:35Z
dc.date.accessioned2023-03-10T16:53:21Z
dc.date.available2016-04-05T19:10:35Z
dc.date.available2023-03-10T16:53:21Z
dc.date.issued2007
dc.identifier.citationY. Shkvarko e I.E. Villalón-Turrubiates (2007). “Dynamical Enhancement of the Large Scale Remote Sensing Imagery for Decision Support in Environmental Resource Management”, en Proceedings of the 18th International Conference of the Information Resources Management Association: Managing Worldwide Operations and Communications with Information Technology, Vancouver Canadá, 2007, pp.1335-1337.es
dc.identifier.isbn978-1-59904-929-8
dc.identifier.urihttps://hdl.handle.net/20.500.12032/68739
dc.descriptionIn this study, we address a new efficient robust optimization approach to large-scale environmental RSSS reconstruction/enhancement as required for remote sensing imaging with multi-spectral array sensors/SAR. First, the problem- oriented robustification of the previously proposed fused Bayesian-regularization (FBR) enhanced imaging method is performed to alleviate its ill-poseness due to system-level and model-level uncertainties. Second, we incorporate the dynamic filtration paradigm into the overall reconstruction technique to enhance the quality of the imagery as it is required for decision support in environmental resource management with dynamic RSSS behavior.es
dc.description.sponsorshipCINVESTAVes
dc.language.isoenges
dc.publisherInformation Resources Management Associationes
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectEnvironmental Remote Sensinges
dc.subjectDynamic Filtrationes
dc.subjectResource Managementes
dc.subjectDecision Supportes
dc.subjectRegularizationes
dc.titleDynamical Enhancement of the Large Scale Remote Sensing Imagery for Decision Support in Environmental Resource Managementes
dc.typeinfo:eu-repo/semantics/conferencePaperes


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