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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 [article]

Voir aussi

• 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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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)  

Public [article]  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 [article]

Voir aussi

• 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)  

Exemplaires(1)

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

Classification of contamination in salt marsh plant using hyperspectral reflectance / M.D. Wilson 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 1088 - 1095 Titre : Classification of contamination in salt marsh plant using hyperspectral reflectance Type de document : Article/Communication Auteurs : M.D. Wilson, Auteur ; S.L. Ustin, Auteur ; D.M. Rocke, Auteur Année de publication : 2004 Article en page(s) : pp 1088 - 1095 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agriculture [Termes IGN] analyse comparative [Termes IGN] contamination [Termes IGN] image hyperspectrale [Termes IGN] marais salé [Termes IGN] pétrole [Termes IGN] pollution des sols [Termes IGN] réflectance spectrale [Termes IGN] réflectance végétale Résumé : (Auteur) In this paper, we compare the classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from five species of salt marsh and two species of crop plants (in four experiments) that have been exposed to varying levels of different heavy metal or petroleum toxicity, with a control treatment for each experiment. If these methodologies work well on leaf-level data, then there is hope that they will also work well on data from air- and spaceborne platforms. The classification methods compared were support vector classification (SVC) of exposed and nonexposed plants based on the spectral reflectance data, and partial least squares compression of the spectral reflectance data followed by classification using logistic discrimination (PLSALD). The statistic we used to compare the effectiveness of the methodologies was the leave-one-out cross-validation estimate of the prediction error. Our results suggest that both techniques perform reasonably well, but that SVC was superior to PLS/LD for use on hyperspectral data and it is worth exploring as a technique for classifying heavy-metal or petroleum exposed plants for the more complicated data from airand spaceborne sensors. Numéro de notice : A2004-195 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.823278 En ligne : https://doi.org/10.1109/TGRS.2003.823278 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26722 [article]

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Nonparametric weighted feature extraction for classification / D.A. Landgrebe 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 1096 - 1105 Titre : Nonparametric weighted feature extraction for classification Type de document : Article/Communication Auteurs : D.A. Landgrebe, Auteur ; B.C. Kuo, Auteur Année de publication : 2004 Article en page(s) : pp 1096 - 1105 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse discriminante [Termes IGN] classificateur non paramétrique [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] matrice [Termes IGN] précision de la classification [Termes IGN] reconnaissance de formes [Termes IGN] réduction géométrique Résumé : (Auteur) In this paper, a new nonparametric feature extraction method is proposed for high-dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired and to reduce the effect of the singularity problem. This is in contrast to parametric discriminant analysis, which usually only can extract L - 1 (number of classes minus one) features. In a real situation, this may not be enough. Second, the nonparametric nature of scatter matrices reduces the effects of outliers and works well even for nonnormal datasets. The new method provides greater weight to samples near the expected decision boundary. This tends to provide for increased classification accuracy. Numéro de notice : A2004-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.825578 En ligne : https://doi.org/10.1109/TGRS.2004.825578 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26723 [article]

Exemplaires(1)

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