ReI

Repositorio Institucional del ITESO

Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging

Manakin: DSpace XMLUI Project v2

Mostrar el registro sencillo del ítem

dc.contributor.author Shkvarko, Yuriy
dc.contributor.author Villalón-Turrubiates, Iván E.
dc.contributor.author Vázquez-Bautista, René
dc.date.accessioned 2016-04-21T21:52:56Z
dc.date.available 2016-04-21T21:52:56Z
dc.date.issued 2007
dc.identifier.citation Yuriy Shkvarko, René Vázquez-Bautista, Iván E. Villalón-Turrubiates, “Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging”, in Advanced Concepts for Intelligent Vision Systems – Lecture Notes in Computer Science, J. Blanc-Talon et al., Ed. Alemania: Springer Berlin Heidelberg, 2007, pp. 109-120. es
dc.identifier.isbn 978-3-540-74606-5
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/11117/3308
dc.description A new fused Bayesian maximum entropy–variational analysis (BMEVA) method for enhanced radar/synthetic aperture radar (SAR) imaging is addressed as required for high-resolution remote sensing (RS) imagery. The variational analysis (VA) paradigm is adapted via incorporating the image gradient flow norm preservation into the overall reconstruction problem to control the geometrical properties of the desired solution. The metrics structure in the corresponding image representation and solution spaces is adjusted to incorporate the VA image formalism and RS model-level considerations; in particular, system calibration data and total image gradient flow power constraints. The BMEVA method aggregates the image model and system-level considerations into the fused SSP reconstruction strategy providing a regularized balance between the noise suppression and gained spatial resolution with the VA-controlled geometrical properties of the resulting solution. The efficiency of the developed enhanced radar imaging approach is illustrated through the numerical simulations with the real-world SAR imagery. es
dc.description.sponsorship Cinvestav es
dc.language.iso eng es
dc.publisher Springer es
dc.relation.ispartofseries Advanced Concepts for Intelligent Vision Systems – Lecture Notes in Computer Science;
dc.rights.uri http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf es
dc.subject Radar/SAR Imaging es
dc.subject Bayesian Maximum Entropy-Variational Analys es
dc.subject Remote Sensing es
dc.title Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging es
dc.type info:eu-repo/semantics/bookPart es
rei.revisor Advanced Concepts for Intelligent Vision Systems – Lecture Notes in Computer Science
rei.peerreviewed Yes es


Archivos en el ítem

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Buscar en todo


Listar

Mi cuenta