Unifying the Experiment Design and Constrained Regularization Paradigms for Reconstructive Imaging with Remote Sensing Data

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dc.contributor.authorShkvarko, Yuriy
dc.contributor.authorLeyva-Montiel, José L.
dc.contributor.authorVillalón-Turrubiates, Iván E.
dc.date.accessioned2016-04-05T22:50:55Z
dc.date.available2016-04-05T22:50:55Z
dc.date.issued2006
dc.descriptionIn this paper, the problem of estimating from a finite set of measurements of the radar remotely sensed complex data signals, the power spatial spectrum pattern (SSP) of the wavefield sources distributed in the environment is cast in the framework of Bayesian minimum risk (MR) paradigm unified with the experiment design (ED) regularization technique. The fused MR-ED regularization of the ill-posed nonlinear inverse problem of the SSP reconstruction is performed via incorporating into the MR estimation strategy the projection-regularization ED constraints. The simulation examples are incorporated to illustrate the efficiency of the proposed unified MR-ED technique.es
dc.description.sponsorshipCINVESTAVes
dc.identifier.citationY. Shkvarko; J.L. Leyva-Montiel; I.E. Villalón-Turrubiates (2006). “Unifying the Experiment Design and Constrained Regularization Paradigms for Reconstructive Imaging with Remote Sensing Data”. Proceedings of the IEEE International Conference on Image Processing (ICIP), Atlanta, EEUU, pp.3241-3244.es
dc.identifier.isbn1-4244-0480-0
dc.identifier.urihttp://hdl.handle.net/11117/3245
dc.language.isoenges
dc.publisherIEEEes
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectSignal Processinges
dc.subjectImage Reconstructiones
dc.subjectRegularizationes
dc.titleUnifying the Experiment Design and Constrained Regularization Paradigms for Reconstructive Imaging with Remote Sensing Dataes
dc.typeinfo:eu-repo/semantics/conferencePaperes
rei.peerreviewedYeses
rei.revisorIEEE International Conference on Image Processing

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