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Improving the modeling of the atmospheric delay in the data analysis of the Intensive VLBI sessions and the impact on the UT1 estimates / Tobias Nilsson in Journal of geodesy, vol 91 n° 7 (July 2017)
[article]
Titre : Improving the modeling of the atmospheric delay in the data analysis of the Intensive VLBI sessions and the impact on the UT1 estimates Type de document : Article/Communication Auteurs : Tobias Nilsson, Auteur ; Benedikt Soja, Auteur ; Kyriakos Balidakis, Auteur ; Maria Karbon, Auteur ; Robert Heinkelmann, Auteur ; Zhiguo Deng, Auteur ; Harald Schuh, Auteur Année de publication : 2017 Article en page(s) : pp 857 - 866 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] analyse de données
[Termes IGN] filtre de Kalman
[Termes IGN] gradient de troposphère
[Termes IGN] interférométrie à très grande base
[Termes IGN] longueur du jour
[Termes IGN] modèle atmosphérique
[Termes IGN] retard troposphérique zénithal
[Termes IGN] temps universelRésumé : (Auteur) The very long baseline interferometry (VLBI) Intensive sessions are typically 1-h and single-baseline VLBI sessions, specifically designed to yield low-latency estimates of UT1-UTC. In this work, we investigate what accuracy is obtained from these sessions and how it can be improved. In particular, we study the modeling of the troposphere in the data analysis. The impact of including external information on the zenith wet delays (ZWD) and tropospheric gradients from GPS or numerical weather prediction models is studied. Additionally, we test estimating tropospheric gradients in the data analysis, which is normally not done. To evaluate the results, we compared the UT1-UTC values from the Intensives to those from simultaneous 24-h VLBI session. Furthermore, we calculated length of day (LOD) estimates using the UT1-UTC values from consecutive Intensives and compared these to the LOD estimated by GPS. We find that there is not much benefit in using external ZWD; however, including external information on the gradients improves the agreement with the reference data. If gradients are estimated in the data analysis, and appropriate constraints are applied, the WRMS difference w.r.t. UT1-UTC from 24-h sessions is reduced by 5% and the WRMS difference w.r.t. the LOD from GPS by up to 12%. The best agreement between Intensives and the reference time series is obtained when using both external gradients from GPS and additionally estimating gradients in the data analysis. Numéro de notice : A2017-298 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-016-0985-7 En ligne : http://doi.org/10.1007/s00190-016-0985-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85333
in Journal of geodesy > vol 91 n° 7 (July 2017) . - pp 857 - 866[article]I’m walking here! Checking the accuracy of an inertial-based pedestrian navigation system with a drone / Marcin Uradzinski in GPS world, vol 28 n° 6 (June 2017)
[article]
Titre : I’m walking here! Checking the accuracy of an inertial-based pedestrian navigation system with a drone Type de document : Article/Communication Auteurs : Marcin Uradzinski, Auteur ; Hang Guo, Auteur ; Clifford Mugnier, Auteur Année de publication : 2017 Article en page(s) : pp 58 - 64 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] drone
[Termes IGN] filtre de Kalman
[Termes IGN] navigation à l'estime
[Termes IGN] navigation pédestre
[Termes IGN] piéton
[Termes IGN] positionnement en intérieurRésumé : (Auteur) Satellite navigation systems have achieved great success in personal positioning applications. Nowadays, GNSS is an essential tool for outdoor navigation, but locating a user’s position in degraded and denied indoor environments is still a challenging task. During the past decade, methodologies have been proposed based on inertial sensors for determining a person’s location to solve this problem. One such solution is a personal pedestrian dead reckoning (PDR) system, which helps in obtaining a seamless indoor/outdoor position. Built-in sensors measure the acceleration to determine pace count and estimate the pace length to predict position with heading information coming from angular sensors such as magnetometers or gyroscopes. PDR positioning solutions find many applications in security monitoring, personal services, navigation in shopping centers and hospitals and for guiding blind pedestrians. Several dead-reckoning navigation algorithms for use with inertial measurement units (IMUs) have been proposed. However, these solutions are very sensitive to the alignment of the sensor units, the inherent instrumental errors, and disturbances from the ambient environment - problems that cause accuracy to decrease over time. In such situations, additional sensors are often used together with an IMU, such as ZigBee radio beacons with position estimated from received signal strength. In this article, we present a PDR indoor positioning system we designed, tested and analyzed. It is based on the pace detection of a foot-mounted IMU, with the use of extended Kalman filter (EKF) algorithms to estimate the errors accumulated by the sensors. Numéro de notice : A2017-294 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85332
in GPS world > vol 28 n° 6 (June 2017) . - pp 58 - 64[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]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]Multilayer NMF for blind unmixing of hyperspectral imagery with additional constraints / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)
[article]
Titre : Multilayer NMF for blind unmixing of hyperspectral imagery with additional constraints Type de document : Article/Communication Auteurs : L. Chen, Auteur ; Shengbo Chen, Auteur ; Xulin Guo, Auteur Année de publication : 2017 Article en page(s) : pp 307 - 316 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] calcul matriciel
[Termes IGN] contrainte spectrale
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] programmation par contraintes
[Termes IGN] réflectanceRésumé : (Auteur) Due to the coincidence of hyperspectral reflectance nonnegativity (and its corresponding abundance) with nonnegative matrix factorization (NMF) methods, NMF has been widely applied to unmix hyperspectral images in recent years. However, many local minima persist because of the nonconvexity of the objective function. Thus, the nonnegativity constraint is not sufficient and additional auxiliary constraints should be applied to objective functions. In this paper, a new approach we call constrained multilayer NMF (CMLNMF), is proposed for hyperspectral data. In this approach, the mixed spectra are regarded as endmember signatures that has been contaminated by multiplicative noise. The purpose of CMLNMF is to eliminate noise by hierarchical processing until the endmember spectra are obtained. Also, the hierarchical processing is self-adaptive to make the algorithm more effective. Furthermore, in each layer two constraints are implemented on the objective function. One is sparseness on the abundance matrix and the other is minimum volume on the spectral matrix. The hierarchical processing separates the abundance matrix into a series of matrices that make the characteristic of sparseness more obvious and meaningful. The proposed algorithm is applied to synthetic data and real hyperspectral data for quantitative evaluation. According to the comparison with other algorithms, CMLNMF has better performance and provides effective solutions for blind unmixing of hyperspectral image data. Numéro de notice : A2017-112 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.14358/PERS.83.4.307 En ligne : https://doi.org/10.14358/PERS.83.4.307 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84590
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 4 (April 2017) . - pp 307 - 316[article]Modified 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)PermalinkCartographie de l'occupation des sols à partir de séries temporelles d'images satellitaires à hautes résolutions : identification et traitement des données mal étiquetées / Charlotte Pelletier (2017)PermalinkPermalinkPermalinkSpringer handbook of Global Navigation Satellite Systems / Peter J.G. Teunissen (2017)PermalinkTélédétection pour l'observation des surfaces continentales, ch. 7. Modèles numériques de terrain à partir de données lidar aéroportées / Clément Mallet (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 2. Observation des surfaces continentales par télédétection micro-onde / Nicolas Baghdadi (2017)PermalinkDetermination of a terrestrial reference frame via Kalman filtering of very long baseline interferometry data / Benedikt Soja in Journal of geodesy, vol 90 n° 12 (December 2016)PermalinkAn approach for estimating time-variable rates from geodetic time series / Olga Didova in Journal of geodesy, vol 90 n° 11 (November 2016)PermalinkAssimilation of SMOS retrievals in the land information system / Clay B. Blankenship in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)Permalink