Remote Sensing Signature Fields Reconstruction via Robust Regularization of Bayesian Minimum Risk Technique

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dc.contributor.authorShkvarko, Yuriy
dc.contributor.authorVillalón-Turrubiates, Iván E.
dc.contributor.authorLeyva-Montiel, José L.
dc.date.accessioned2016-04-22T22:42:48Z
dc.date.available2016-04-22T22:42:48Z
dc.date.issued2007
dc.descriptionThe robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. The problem- oriented modification of the previously proposed fused Bayesian-regularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill- poseness due to system-level and model-level uncertainties. We report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the developed method.es
dc.description.sponsorshipCinvestaves
dc.identifier.citationYuriy V. Shkvarko, Iván E. Villalón-Turrubiates, José L. Leyva-Montiel, “Remote Sensing Signature Fields Reconstruction via Robust Regularization of Bayesian Minimum Risk Technique”, in Proceedings of the 2nd IEEE International Workshop on Computational Advances in Multi-Sensor adaptive processing (CAMSAP), Islas Vírgenes EE.UU., 2007, pp. 237-240.es
dc.identifier.isbn978-1-4244-1713-1
dc.identifier.urihttp://hdl.handle.net/11117/3312
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.relation.ispartofseriesIEEE International Workshop on Computational Advances in Multi-Sensor adaptive processing (CAMSAP);2nd
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectSignal Processinges
dc.subjectSystem Fusiones
dc.subjectImage Reconstructiones
dc.subjectRegularizationes
dc.titleRemote Sensing Signature Fields Reconstruction via Robust Regularization of Bayesian Minimum Risk Techniquees
dc.typeinfo:eu-repo/semantics/conferencePaperes
rei.peerreviewedYeses
rei.revisor2nd IEEE International Workshop on Computational Advances in Multi-Sensor adaptive processing (CAMSAP)

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