Descripteur
Documents disponibles dans cette catégorie (1282)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Multivariate analysis of GPS position time series of JPL second reprocessing campaign / Ali Reza Amiri-Simkooei in Journal of geodesy, vol 91 n° 6 (June 2017)
![]()
[article]
Titre : Multivariate analysis of GPS position time series of JPL second reprocessing campaign Type de document : Article/Communication Auteurs : Ali Reza Amiri-Simkooei, Auteur ; T.H. Mohammadloo, Auteur ; Donald F. Argus, Auteur Année de publication : 2017 Article en page(s) : pp 685 - 704 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse multivariée
[Termes IGN] bruit blanc
[Termes IGN] bruit rose
[Termes IGN] campagne GPS
[Termes IGN] centre de phase
[Termes IGN] coordonnées GPS
[Termes IGN] série temporelle
[Termes IGN] terme de Chandler
[Vedettes matières IGN] Traitement de données GNSSRésumé : (Auteur) The second reprocessing of all GPS data gathered by the Analysis Centers of IGS was conducted in late 2013 using the latest models and methodologies. Improved models of antenna phase center variations and solar radiation pressure in JPL’s reanalysis are expected to significantly reduce errors. In an earlier work, JPL estimates of position time series, termed first reprocessing campaign, were examined in terms of their spatial and temporal correlation, power spectra, and draconitic signal. Similar analyses are applied to GPS time series at 89 and 66 sites of the second reanalysis with the time span of 7 and 21 years, respectively, to study possible improvements. Our results indicate that the spatial correlations are reduced on average by a factor of 1.25. While the white and flicker noise amplitudes for all components are reduced by 29–56 %, the random walk amplitude is enlarged. The white, flicker, and random walk noise amount to rate errors of, respectively, 0.01, 0.12, and 0.09 mm/yr in the horizontal and 0.04, 0.41 and 0.3 mm/yr in the vertical. Signals reported previously, such as those with periods of 13.63, 14.76, 5.5, and 351.4 / n for n=1,2,…,8 days, are identified in multivariate spectra of both data sets. The oscillation of the draconitic signal is reduced by factors of 1.87, 1.87, and 1.68 in the east, north and up components, respectively. Two other signals with Chandlerian period and a period of 380 days can also be detected. Numéro de notice : A2017-297 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-016-0991-9 En ligne : http://dx.doi.org/10.1007/s00190-016-0991-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85328
in Journal of geodesy > vol 91 n° 6 (June 2017) . - pp 685 - 704[article]On the short-term temporal variations of GNSS receiver differential phase biases / Baocheng Zhang in Journal of geodesy, vol 91 n° 5 (May 2017)
![]()
[article]
Titre : On the short-term temporal variations of GNSS receiver differential phase biases Type de document : Article/Communication Auteurs : Baocheng Zhang, Auteur ; Peter J.G. Teunissen, Auteur ; Yunbin Yuan, Auteur Année de publication : 2017 Article en page(s) : pp 563 – 572 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] données GNSS
[Termes IGN] erreur de phase
[Termes IGN] erreur systématique
[Termes IGN] positionnement différentiel
[Termes IGN] température au sol
[Termes IGN] teneur totale en électrons
[Termes IGN] trajet multiple
[Termes IGN] variation temporelleRésumé : (auteur) As a first step towards studying the ionosphere with the global navigation satellite system (GNSS), leveling the phase to the code geometry-free observations on an arc-by-arc basis yields the ionospheric observables, interpreted as a combination of slant total electron content along with satellite and receiver differential code biases (DCB). The leveling errors in the ionospheric observables may arise during this procedure, which, according to previous studies by other researchers, are due to the combined effects of the code multipath and the intra-day variability in the receiver DCB. In this paper we further identify the short-term temporal variations of receiver differential phase biases (DPB) as another possible cause of leveling errors. Our investigation starts by the development of a method to epoch-wise estimate between-receiver DPB (BR-DPB) employing (inter-receiver) single-differenced, phase-only GNSS observations collected from a pair of receivers creating a zero or short baseline. The key issue for this method is to get rid of the possible discontinuities in the epoch-wise BR-DPB estimates, occurring when satellite assigned as pivot changes. Our numerical tests, carried out using Global Positioning System (GPS, US GNSS) and BeiDou Navigation Satellite System (BDS, Chinese GNSS) observations sampled every 30 s by a dedicatedly selected set of zero and short baselines, suggest two major findings. First, epoch-wise BR-DPB estimates can exhibit remarkable variability over a rather short period of time (e.g. 6 cm over 3 h), thus significant from a statistical point of view. Second, a dominant factor driving this variability is the changes of ambient temperature, instead of the un-modelled phase multipath. Numéro de notice : A2017-228 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-016-0983-9 En ligne : http://dx.doi.org/10.1007/s00190-016-0983-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85109
in Journal of geodesy > vol 91 n° 5 (May 2017) . - pp 563 – 572[article]Space-wise approach for airborne gravity data modelling / Daniele Sampietro in Journal of geodesy, vol 91 n° 5 (May 2017)
![]()
[article]
Titre : Space-wise approach for airborne gravity data modelling Type de document : Article/Communication Auteurs : Daniele Sampietro, Auteur ; M. Cappon, Auteur ; A. H. Mansi, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 535 – 545 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] collocation par moindres carrés
[Termes IGN] covariance
[Termes IGN] filtre de Wiener
[Termes IGN] géoïde gravimétrique
[Termes IGN] gravimétrie aérienne
[Termes IGN] modèle de géopotentielRésumé : (auteur) Regional gravity field modelling by means of remove-compute-restore procedure is nowadays widely applied in different contexts: it is the most used technique for regional gravimetric geoid determination, and it is also used in exploration geophysics to predict grids of gravity anomalies (Bouguer, free-air, isostatic, etc.), which are useful to understand and map geological structures in a specific region. Considering this last application, due to the required accuracy and resolution, airborne gravity observations are usually adopted. However, due to the relatively high acquisition velocity, presence of atmospheric turbulence, aircraft vibration, instrumental drift, etc., airborne data are usually contaminated by a very high observation error. For this reason, a proper procedure to filter the raw observations in both the low and high frequencies should be applied to recover valuable information. In this work, a software to filter and grid raw airborne observations is presented: the proposed solution consists in a combination of an along-track Wiener filter and a classical Least Squares Collocation technique. Basically, the proposed procedure is an adaptation to airborne gravimetry of the Space-Wise approach, developed by Politecnico di Milano to process data coming from the ESA satellite mission GOCE. Among the main differences with respect to the satellite application of this approach, there is the fact that, while in processing GOCE data the stochastic characteristics of the observation error can be considered a-priori well known, in airborne gravimetry, due to the complex environment in which the observations are acquired, these characteristics are unknown and should be retrieved from the dataset itself. The presented solution is suited for airborne data analysis in order to be able to quickly filter and grid gravity observations in an easy way. Some innovative theoretical aspects focusing in particular on the theoretical covariance modelling are presented too. In the end, the goodness of the procedure is evaluated by means of a test on real data retrieving the gravitational signal with a predicted accuracy of about 0.4 mGal. Numéro de notice : A2017-227 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-016-0981-y Date de publication en ligne : 18/12/2016 En ligne : https://doi.org/10.1007/s00190-016-0981-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85108
in Journal of geodesy > vol 91 n° 5 (May 2017) . - pp 535 – 545[article]Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data / André Dittrich in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
![]()
[article]
Titre : Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data Type de document : Article/Communication Auteurs : André Dittrich, Auteur ; Martin Weinmann, Auteur ; Stefan Hinz, Auteur Année de publication : 2017 Article en page(s) : pp 195 – 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bruit (théorie du signal)
[Termes IGN] calcul tensoriel
[Termes IGN] discrétisation
[Termes IGN] données localisées 3D
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] lasergrammétrie
[Termes IGN] méthode robuste
[Termes IGN] restitution lasergrammétrique
[Termes IGN] semis de points
[Termes IGN] valeur propreRésumé : (auteur) In photogrammetry, remote sensing, computer vision and robotics, a topic of major interest is represented by the automatic analysis of 3D point cloud data. This task often relies on the use of geometric features amongst which particularly the ones derived from the eigenvalues of the 3D structure tensor (e.g. the three dimensionality features of linearity, planarity and sphericity) have proven to be descriptive and are therefore commonly involved for classification tasks. Although these geometric features are meanwhile considered as standard, very little attention has been paid to their accuracy and robustness. In this paper, we hence focus on the influence of discretization and noise on the most commonly used geometric features. More specifically, we investigate the accuracy and robustness of the eigenvalues of the 3D structure tensor and also of the features derived from these eigenvalues. Thereby, we provide both analytical and numerical considerations which clearly reveal that certain features are more susceptible to discretization and noise whereas others are more robust. Numéro de notice : A2017-117 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.012 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84512
in ISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 195 – 208[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017043 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017042 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Deep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
![]()
[article]
Titre : Deep supervised and contractive neural network for SAR image classification Type de document : Article/Communication Auteurs : Jie Geng, Auteur ; Hongyu Wang, Auteur ; Jianchao Fan, Auteur ; Xiaorui Ma, Auteur Année de publication : 2017 Article en page(s) : pp 2442 - 2459 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] algorithme Graph-Cut
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de déchatoiement
[Termes IGN] filtre de Gabor
[Termes IGN] image radar moirée
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)Résumé : (Auteur) The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches. Numéro de notice : A2017-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2645226 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2645226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84748
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2442 - 2459[article]Global ionosphere maps based on GNSS, satellite altimetry, radio occultation and DORIS / Peng Chen in GPS solutions, vol 21 n° 2 (April 2017)
PermalinkIntegrating uncertainty propagation in GNSS radio occultation retrieval: From bending angle to dry-air atmospheric profiles / Jakob Schwarz in Earth and space science, vol 4 n° 4 (April 2017)
PermalinkIonospheric error contribution to GNSS single-frequency navigation at the 2014 solar maximum / Raul Orus Perez in Journal of geodesy, vol 91 n° 4 (April 2017)
PermalinkMultilayer NMF for blind unmixing of hyperspectral imagery with additional constraints / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)
PermalinkPerformance evaluation of GNSS-TEC estimation techniques at the grid point in middle and low latitudes during different geomagnetic conditions / O. E. Abe in Journal of geodesy, vol 91 n° 4 (April 2017)
PermalinkAdaptive time-variant adjustment for the positioning errors of a mobile mapping platform in GNSS-hostile areas / Jiawei Han in Survey review, vol 49 n° 352 (March 2017)
PermalinkHyperspectral SAR / Matthew Ferrara in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkModified residual method for the estimation of noise in hyperspectral images / Asad Mahmood in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
PermalinkMulti-GNSS precise point positioning (MGPPP) using raw observations / Teng Liu in Journal of geodesy, vol 91 n° 3 (March 2017)
PermalinkNew point matching algorithm using sparse representation of image patch feature for SAR image registration / Jianwei Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
Permalink