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dc.contributor.authorVillalón-Turrubiates, Iván E.
dc.contributor.authorShkvarko, Yuriy
dc.date.accessioned2016-04-21T17:52:18Z
dc.date.accessioned2023-03-16T20:09:01Z
dc.date.available2016-04-21T17:52:18Z
dc.date.available2023-03-16T20:09:01Z
dc.date.issued2006
dc.identifier.citationIvan E. Villalon-Turrubiates, Yuriy V. Shkvarko, “Cognitive Reconstructive Remote Sensing for Decision Support in Environmental Resource Management”, in Proceedings of the 18th International Conference of the Information Resources Management Association (IRMA): Emerging Trends and Challenges in Information Technology Management, Washington D.C. EE.UU., 2006, pp. 978-980.es
dc.identifier.isbn1-59904-019-0
dc.identifier.urihttps://hdl.handle.net/20.500.12032/72498
dc.descriptionIn this paper, the problem of reconstruction of different characteristic signatures (CSs) of the monitored environmental scenes from the multi-spectral remotely sensed data is cast in the unified framework of the statistically optimal Bayesian inference making strategy aggregated with the proposed cognitive descriptive regularization paradigm. The reconstructed CS maps are then treated as sufficient statistical data required for performing the environmental resource management tasks. Simulation examples with the real-world remote sensing data are provided to illustrate the efficiency of the proposed approach.es
dc.description.sponsorshipCinvestaves
dc.language.isoenges
dc.publisherInformation Resources Management Associationes
dc.relation.ispartofseriesInternational Conference of the Information Resources Management Association (IRMA);18th
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectEnvironmental Remote Sensinges
dc.subjectResource Management, Decision Supportes
dc.subjectRegularizationes
dc.titleCognitive Reconstructive Remote Sensing for Decision Support in Environmental Resource Managementes
dc.typeinfo:eu-repo/semantics/conferencePaperes


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