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Prediction of the error induced by topography in satellite microwave radiometric observations / Luca Pulvirenti in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 9 (September 2011) . - pp 3180 - 3188 Titre : Prediction of the error induced by topography in satellite microwave radiometric observations Type de document : Article/Communication Auteurs : Luca Pulvirenti, Auteur ; Nazzareno Pierdicca, Auteur ; F. Silvio, Auteur Année de publication : 2011 Article en page(s) : pp 3180 - 3188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Alpes [Termes IGN] analyse comparative [Termes IGN] angle d'incidence [Termes IGN] bande C [Termes IGN] bande L [Termes IGN] classification par réseau neuronal [Termes IGN] humidité du sol [Termes IGN] image radar [Termes IGN] image satellite [Termes IGN] montagne [Termes IGN] plaine [Termes IGN] régression [Termes IGN] relief [Termes IGN] sol nu [Termes IGN] télédétection en hyperfréquence [Termes IGN] topographie [Termes IGN] valeur radiométrique Résumé : (Auteur) A numerical simulator of satellite microwave radiometric observations of mountainous scenes, developed in a previous study, has been used to predict the relief effects on the measurements of a spaceborne radiometer. For this purpose, the trends of the error due to topography, i.e., the difference between the antenna temperature calculated for a topographically variable surface and that computed for a flat terrain versus the parameters representing the relief, have been analyzed. The analysis has been mainly performed for a mountainous area in the Alps by assuming a simplified land-cover scenario consisting of bare terrain with two roughness conditions (smooth and rough soils) and considering L- and C-bands, i.e., those most suitable for soil moisture retrieval. The results have revealed that the error in satellite microwave radiometric observations is particularly correlated to the mean values of the height and slope of the radiometric pixel, as well as to the standard deviations of the aspect angle and local incidence angle. Both a regression analysis and a neural-network approach have been applied to estimate the error as a function of the parameters representing the relief, using the simulator to build training and test sets. The prediction of the topography effects and their correction in radiometric images have turned out to be feasible, at least for the scenarios considered in this study. Numéro de notice : A2011-361 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2096514 Date de publication en ligne : 06/01/2011 En ligne : https://doi.org/10.1109/TGRS.2010.2096514 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31140 [article]

Exemplaires(1)

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Three-dimensional humidity retrieval using a network of compact microwave radiometers to correct for variations in wet tropospheric path delay in spaceborne interferometric SAR imagery / S. Sahoo in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 9 (September 2011) . - pp 3281 - 3290 Titre : Three-dimensional humidity retrieval using a network of compact microwave radiometers to correct for variations in wet tropospheric path delay in spaceborne interferometric SAR imagery Type de document : Article/Communication Auteurs : S. Sahoo, Auteur ; C. Reising, Auteur ; S. Padmanabhan, Auteur ; et al., Auteur Année de publication : 2011 Article en page(s) : pp 3281 - 3290 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] dégradation du signal [Termes IGN] distribution spatiale [Termes IGN] image radar moirée [Termes IGN] image spatiale [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] propagation troposphérique [Termes IGN] teneur en vapeur d'eau [Termes IGN] vapeur d'eau [Termes IGN] variation temporelle Résumé : (Auteur) Spaceborne interferometric synthetic aperture radar (InSAR) imaging has been used for over a decade to monitor tectonic movements and landslides, as well as to improve digital elevation models. However, InSAR is affected by variations in round-trip propagation delay due to changes in ionospheric total electron content and in tropospheric humidity and temperature along the signal path. One of the largest sources of uncertainty in estimates of tropospheric path delay is the spatial and temporal variability of water vapor density, which currently limits the quality of InSAR products. This problem can be partially addressed by using a number of SAR interferograms from subsequent satellite overpasses to reduce the degradation in the images or by analyzing a long time series of interferometric phases from permanent scatterers. However, if there is a sudden deformation of the Earth's surface, the detection of which is one of the principal objectives of InSAR measurements over land, the effect of water vapor variations cannot be removed, reducing the quality of the interferometric products. In those cases, high-resolution information on the atmospheric water vapor content and its variation with time can be crucial to mitigate the effect of wet-tropospheric path delay variations. This paper describes the use of a ground-based microwave radiometer network to retrieve 3D water vapor density with fine spatial and temporal resolution, which can be used to reduce InSAR ambiguities due to changes in wet-tropospheric path delay. Retrieval results and comparisons between the integrated water vapor measured by the radiometer network and satellite data are presented. Numéro de notice : A2011-362 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2119400 Date de publication en ligne : 16/05/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2119400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31141 [article]

Exemplaires(1)

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Simultaneous denoising and intrinsic order selection in hyperspectral imaging / M. Farzam in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 9 (September 2011) . - pp 3423 - 3436 Titre : Simultaneous denoising and intrinsic order selection in hyperspectral imaging Type de document : Article/Communication Auteurs : M. Farzam, Auteur ; S. Beheshti, Auteur Année de publication : 2011 Article en page(s) : pp 3423 - 3436 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bruit atmosphérique [Termes IGN] classification automatique [Termes IGN] estimation de précision [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] propagation d'erreur [Termes IGN] rapport signal sur bruit Résumé : (Auteur) In this paper, we address the problem of order selection in noisy hyperspectral applications. In conventional unmixing methods, this problem has been divided into two separate processes of order selection and unmixing. Order selection methods generally use a denoising approach at the beginning stage. The data in this case pass through three stages: denoising, order selection, and unmixing. Each of these steps mainly aims to optimize a different criterion independently. In addition, any error created in the denoising process will be propagated not only to the order selection stage but also consequently to the unmixing results. Commonly used denoising methods such as eigenvalue-decomposition-based methods, e.g., singular-value-decomposition-based methods, provide a threshold value to separate the noise from the signal. These approaches are heavily sensitive to the threshold value and signal-to-noise ratio (SNR). Moreover, these methods tend to lose their efficiency rapidly for lower SNRs. Note that both the denoising step and the dimension estimation step aim to provide the optimum estimate of the same noiseless data. Consequently, adopting a simultaneous denoising and dimension estimation method with a goal to provide the optimum estimate of the desired noiseless data is rational. This process not only avoids possible error propagations from the denoising stage to the dimension estimation stage but also unifies the optimization criteria that were used in each of these steps. In this paper, a simultaneous denoising and dimension estimation method is introduced. The approach is based on minimizing the estimated mean square error. Minimization is done by comparing the estimated data in a range of subspaces dictated by a simultaneous process. Minimizing the error at once, the proposed method denoises the data and provides the optimum dimension simultaneously. Owing to the parallel processing of denoising and dimension estimation, the simulation results show the advantages of the proposed method over some of the state-of-the-art approaches and illustrate a substantial performance, particularly for cases with a lower SNR. Numéro de notice : A2011-363 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2119400 Date de publication en ligne : 29/04/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2119400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31142 [article]

Exemplaires(1)

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A quality prediction method for building model reconstruction using LiDAR data and topographic maps / R. You in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 9 (September 2011) . - pp 3471 - 3480 Titre : A quality prediction method for building model reconstruction using LiDAR data and topographic maps Type de document : Article/Communication Auteurs : R. You, Auteur ; B. Lin, Auteur Année de publication : 2011 Article en page(s) : pp 3471 - 3480 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] calcul tensoriel [Termes IGN] carte topographique [Termes IGN] conflation [Termes IGN] données lidar [Termes IGN] fusion de données multisource [Termes IGN] indicateur de qualité [Termes IGN] reconstruction 3D du bâti [Termes IGN] toit Résumé : (Auteur) This paper integrates light detection and ranging (LiDAR) data and topographic maps and predicts the quality of 3D building model reconstruction. In this paper, the tensor voting algorithm and a region-growing method are adopted to extract building roof planes and structural lines from LiDAR data, and a robust least squares method is applied to register LiDAR data with building outlines obtained from topographic maps. The minimal square sum of the separations of the most peripheral points to building outlines is adopted as the criterion for determining the transformation parameters in order to improve the efficiency of data fusion. After registration, a novel quality indicator of data fusion based on the tensor analysis of residuals is derived in order to evaluate the quality of the automatic reconstruction of 3D building models. Finally, an actual LiDAR data set and its corresponding topographic map demonstrate the fusion procedure and the quality of the predictions related to automatic model reconstruction. Numéro de notice : A2011-364 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2128326 Date de publication en ligne : 12/05/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2128326 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31143 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011091	RAB	Revue	Centre de documentation	En réserve L003	Disponible