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Unifying regularization and Bayesian estimation methods for enhanced imaging with remotely sensed data - part 2: implementation and performance issues / Y.V. Shkvarko in IEEE Transactions on geoscience and remote sensing, vol 42 n° 5 (May 2004)

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inIEEE Transactions on geoscience and remote sensing > vol 42 n° 5 (May 2004) . - pp 932 - 940

Titre :	Unifying regularization and Bayesian estimation methods for enhanced imaging with remotely sensed data - part 2: implementation and performance issues
Type de document :	Article/Communication
Auteurs :	Y.V. Shkvarko, Auteur
Année de publication :	2004
Article en page(s) :	pp 932 - 940
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image [Termes IGN] entropie [Termes IGN] équation non linéaire [Termes IGN] estimation bayesienne [Termes IGN] image ERS-SAR [Termes IGN] implémentation (informatique) [Termes IGN] inversion [Termes IGN] méthode robuste
Résumé :	(Auteur) The fused Bayesian-regularization (FBR) method from a companion paper provides a rigorous theoretical formalism for optimal estimation of the power spatial spectrum pattern (SSP) of the wave field scattered from the probing surface as it is required for enhanced radar imaging of the remotely sensed scenes. Being nonlinear and solution-dependent, the optimal FBR method requires extremely complex nonlinear solution-dependent operator inversions and, therefore, cannot be recommended as a numerically realizable estimator of the SSP. Here, we design a family of robust easy-to-implement FBR algorithms, provide the relevant computational recipes, and discuss their performances. We comment on the practical aspects of the robustified FBR estimators, such as numerical implementation and improvement in the output SNR. The advantage in using the proposed robust FBR method is demonstrated through simulations of enhancing the SAR images formed using the conventional matched filtering of the trajectory signal.
Numéro de notice :	A2004-194
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1109/TGRS.2003.823279
En ligne :	https://doi.org/10.1109/TGRS.2003.823279
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=26721

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Unifying regularization and Bayesian estimation methods for enhanced imaging with remotely sensed data - part 1: theory / Y.V. Shkvarko in IEEE Transactions on geoscience and remote sensing, vol 42 n° 5 (May 2004)

Public

[article]
inIEEE Transactions on geoscience and remote sensing > vol 42 n° 5 (May 2004) . - pp 923 - 931

Titre :	Unifying regularization and Bayesian estimation methods for enhanced imaging with remotely sensed data - part 1: theory
Type de document :	Article/Communication
Auteurs :	Y.V. Shkvarko, Auteur
Année de publication :	2004
Article en page(s) :	pp 923 - 931
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image [Termes IGN] accentuation d'image [Termes IGN] estimation bayesienne [Termes IGN] fusion d'images
Résumé :	(Auteur) The problem of estimating, from one sampled realization of the remotely sensed data signal, the power spatial spectrum pattern (SSP) of the wave field scattered from the probing surface is treated as it is required for enhanced radar imaging of the remotely sensed scenes. Specifically, we propose to unify the Bayesian estimation strategy with the maximum-entropy (ME) information-theoretic principle for incorporating the prior knowledge through developing the fused Bayesian-regularization (FBR) technique for SSP estimation. The first aspect of the proposed approach concerns the ME-based incorporating the a priori information about the geometrical properties of an image to tailor the metrics structure in the solution space to the problem at hand. The second aspect alleviates the problem ill-poseness associated with preserving the boundary values, calibration, and spectral a priori fixed model properties of an image through the regularizing projection constraints imposed on the solution. When applied to SSP estimation without incorporating the metrics and regularization considerations, the procedure leads to the previously derived maximum-likelihood method. When such considerations are incorporated, the optimal FBR technique leads to a new nonlinear imaging algorithm that implies adaptive formation of the second-order sufficient statistics of the data, their smoothing, and projection applying the composite regularizing window operator. We provide analytical techniques to find these statistics and windows, and the optimal FBR estimator itself. Numerical recipes, performance issues, and simulation examples are treated in a companion paper.
Numéro de notice :	A2004-193
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1109/TGRS.2003.823281
En ligne :	https://doi.org/10.1109/TGRS.2003.823281
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=26720

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-04051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-04051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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Accès directs

2014 -2024 IGN

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