Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2623
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dc.contributor.authorDaba, Jihad S.en_US
dc.contributor.authorBell, Mark Ren_US
dc.date.accessioned2020-12-23T09:16:57Z-
dc.date.available2020-12-23T09:16:57Z-
dc.date.issued2003-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/2623-
dc.description.abstractThis paper presents stochastic models and estimation algorithms for speckled images, with an emphasis on synthetic-aperture-radar images, and where the speckle may not be fully developed. We treat speckle from a novel point of view: as a carrier of useful surface information rather than as contaminating noise. The stochastic models for surface scattering are based on a doubly stochastic marked Poisson point process. For each of these surface-scattering statistical models, we present estimation algorithms to determine the average surface reflectivity and scatterer density within a resolution cell, using intensity measurements of speckled images. We show that the maximum-likelihood estimator is optimal in the sense that the variance of the error is the smallest possible using any conceivable estimate having the same bias with the same data.en_US
dc.format.extent3 p.en_US
dc.language.isoengen_US
dc.titleSynthetic aperture radar surface reflectivity estimation using a marked point-process speckle modelen_US
dc.typeJournal Articleen_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.description.volume42en_US
dc.description.issue12en_US
dc.description.startpage478en_US
dc.description.endpage481en_US
dc.date.catalogued2017-11-13-
dc.description.statusPublisheden_US
dc.identifier.OlibID174936-
dc.relation.ispartoftextThe journal of optical engineeringen_US
dc.provenance.recordsourceOliben_US
Appears in Collections:Department of Electrical Engineering
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