Centre dedocumentation
scientifique

Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series / Kevin Gobron in Journal of geodesy, vol 96 n° 7 (July 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 7 (July 2022) . - n° 46 Titre : Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series Type de document : Article/Communication Auteurs : Kevin Gobron, Auteur ; Paul Rebischung , Auteur ; Olivier de Viron, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] analyse de variance [Termes IGN] bruit blanc [Termes IGN] fréquence [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] modèle de Gauss-Markov [Termes IGN] modèle stochastique [Termes IGN] série temporelle [Termes IGN] vitesse Résumé : (auteur) Understanding and modelling the properties of the stochastic variations in geodetic time series is crucial to obtain realistic uncertainties for deterministic parameters, e.g., long-term velocities, and helpful in characterizing non-modelled processes. With the increasing span of geodetic time series, it is expected that additional observations would help better understand the low-frequency properties of these stochastic variations. In the meantime, recent studies evidenced that the choice of the functional model for the time series biases the assessment of these low-frequency stochastic properties. In particular, frequent offsets in position time series can hinder the evaluation of the noise level at low frequencies and prevent the detection of possible random-walk-type variability. This study investigates the ability of the Maximum Likelihood Estimation (MLE) method to correctly retrieve low-frequency stochastic properties of geodetic time series in the presence of frequent offsets. We show that part of the influence of offsets reported by previous studies results from the MLE method estimation biases. These biases occur even when all offset epochs are correctly identified and accounted for in the trajectory model. They can cause a dramatic underestimation of deterministic parameter uncertainties. We show that one can avoid biases using the Restricted Maximum Likelihood Estimation (RMLE) method. Yet, even when using the RMLE method or equivalent, adding offsets to the trajectory model inevitably blurs the estimated low-frequency properties of geodetic time series by increasing low-frequency stochastic parameter uncertainties more than other stochastic parameters. Numéro de notice : A2022-519 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01634-9 Date de publication en ligne : 29/06/2022 En ligne : https://doi.org/10.1007/s00190-022-01634-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101072 [article]

Improving remote sensing classification: A deep-learning-assisted model / Tsimur Davydzenka in Computers & geosciences, vol 164 (July 2022)  

Public [article]  inComputers & geosciences > vol 164 (July 2022) . - n° 105123 Titre : Improving remote sensing classification: A deep-learning-assisted model Type de document : Article/Communication Auteurs : Tsimur Davydzenka, Auteur ; Pejman Tahmasebi, Auteur ; Mark Carroll, Auteur Année de publication : 2022 Article en page(s) : n° 105123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image à haute résolution [Termes IGN] modèle stochastique [Termes IGN] précision de la classification Résumé : (auteur) In many industries and applications, obtaining and classifying remote sensing imagery plays a crucial role. The accuracy of classification, in particular the machine learning methods, mainly depends on a multitude of factors, among which one of the most important ones is the amount of training data. Obtaining sufficient amounts of training data, however, can be very difficult or costly, and one must find alternative ways to improve the accuracy of predictions. To this end, a possible solution that we provide in this study is to use a stochastic method for producing variations of the training images that will retain the important class-wide features and thereby enrich the machine learning's “understanding” of the variabilities. As such, we applied a stochastic algorithm to produce additional realizations of the limited input imagery and thereby significantly increase the final overall accuracy in a deep learning method. We found that by enlarging the initial training set by additional realizations, we are able to consistently improve classification accuracy, compared with generic image augmentation approaches. The results of this study show that there is a great opportunity to increase the accuracy of predictions when enough data are not available. Numéro de notice : A2022-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105123 Date de publication en ligne : 29/04/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100672 [article]

Ajustement en bloc des données de stations totales et de récepteurs GNSS dans les études de déformation / Joël Van Cranenbroeck in XYZ, n° 171 (juin 2022)

Public [article]  inXYZ > n° 171 (juin 2022) . - pp 25 - 32 Titre : Ajustement en bloc des données de stations totales et de récepteurs GNSS dans les études de déformation Type de document : Article/Communication Auteurs : Joël Van Cranenbroeck, Auteur ; Nicolas Van Cranenbroeck, Auteur Année de publication : 2022 Article en page(s) : pp 25 - 32 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Topographie [Termes IGN] auscultation d'ouvrage [Termes IGN] données GNSS [Termes IGN] méthode des moindres carrés [Termes IGN] modèle fonctionnel [Termes IGN] modèle stochastique [Termes IGN] surveillance d'ouvrage [Termes IGN] tachéomètre électronique Résumé : (Auteur) En 1988, le département de la géodésie de l’Institut géographique national de Belgique décida de contribuer aux relevés topographiques des zones urbaines en proposant deux innovations originales. Les nouvelles bases de données SIG urbaines bénéficiaient à cette époque d’un grand engouement de la part des pouvoirs publics. En général, les méthodes photogrammétriques étaient plébiscitées pour leur efficacité en termes de réalisation, mais au niveau de la qualité de la restitution ainsi que de l’interprétation des objets spatiaux, on était loin des espérances. Il était donc toujours indispensable de recourir à la topographie, non seulement pour améliorer la précision de certaines zones, mais également pour la mise à jour de ces bases de données année après année. La topographie avait vu également son évolution technique s’améliorer avec les nouvelles stations totales et les systèmes de traitement des données sur base de codage des informations attributaires des points, lignes et surfaces. Numéro de notice : A2022-522 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101067 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
112-2022022	SL	Revue	Centre de documentation	Revues en salle	En circulation Exclu du prêt
112-2022021	SL	Revue	Centre de documentation	Revues en salle	Disponible

Characteristics of the BDS-3 multipath effect and mitigation methods using precise point positioning / Ran Lu in GPS solutions, vol 26 n° 2 (April 2022)  

Public [article]  inGPS solutions > vol 26 n° 2 (April 2022) . - n° 41 Titre : Characteristics of the BDS-3 multipath effect and mitigation methods using precise point positioning Type de document : Article/Communication Auteurs : Ran Lu, Auteur ; Wen Chen, Auteur ; Chenglong Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] constellation BeiDou [Termes IGN] correction du trajet multiple [Termes IGN] modèle stochastique [Termes IGN] orbite géostationnaire [Termes IGN] orbite terrestre [Termes IGN] positionnement par BeiDou [Termes IGN] positionnement ponctuel précis [Termes IGN] répétabilité [Termes IGN] trajet multiple Résumé : (auteur) Multipath effect is one of the main challenges of precise point positioning (PPP) in complex environments. Nowadays, the BeiDou global navigation satellite system (BDS-3) constellation was fully operational. We evaluated the multipath characteristics of BDS-3 open-service signals. The results indicate that the B2a signal had the best anti-multipath performance, and B1C signal had the worst capability. Since BDS-3 satellites with different orbital types have different orbital repeat time, the traditional method based on multipath time-domain repeatability is complicated to alleviate the multipath error on BDS-3 satellites. In contrast, the multipath spatial-domain repeatability method does not need to calculate the orbital repeat times and is only related to the position of the satellite in the sky. It has the advantages of simple algorithm and easy implementation. We selected a multipath hemispherical map (MHM) and a MHM based on trend-surface analysis (T-MHM) to evaluate the effects of BDS-3 PPP multipath correction. The positioning results for the inclined geosynchronous orbit (IGSO) and medium earth orbit (MEO) satellites, which were separately modeled and corrected, are slightly better than those obtained when they were modeled and corrected together. Compared with the uncorrected multipath, the positioning accuracy of B1I/B3I and B1C/B2a ionospheric-free (IF) combinations using the MHM can be improved by 52.7% and 51.6% and the convergence time can be shortened by 48.6% and 57.5%, respectively. The positioning accuracy of B1I/B3I and B1C/B2a IF combinations using the T-MHM can be improved by 67% and 66.9% and the convergence time can be shortened by 69.3% and 76.5%, respectively. The T-MHM introduces trend-surface analysis to model the spatial variation of the multipath inside the grid, which effectively alleviates high-frequency and low-frequency multipath. This study is of great significance for further improvements to the application of BDS-3 in complex environments. Numéro de notice : A2022-106 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01227-1 Date de publication en ligne : 24/01/2022 En ligne : https://doi.org/10.1007/s10291-022-01227-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99606 [article]

Efficient variance component estimation for large-scale least-squares problems in satellite geodesy / Yufeng Nie in Journal of geodesy, vol 96 n° 2 (February 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 2 (February 2022) . - n° 13 Titre : Efficient variance component estimation for large-scale least-squares problems in satellite geodesy Type de document : Article/Communication Auteurs : Yufeng Nie, Auteur ; Yunzhong Shen, Auteur ; Roland Pail, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] analyse de variance [Termes IGN] champ de pesanteur terrestre [Termes IGN] estimation statistique [Termes IGN] GRACE [Termes IGN] méthode de Monte-Carlo [Termes IGN] méthode des moindres carrés [Termes IGN] modèle stochastique Résumé : (auteur) Efficient Variance Component Estimation (VCE) is significant to optimal data combination in large-scale least-squares problems as those encountered in satellite geodesy, where millions of observations are jointly processed to estimate a huge number of unknown parameters. In this paper, an efficient VCE algorithm with rigorous trace calculation is proposed based on the local–global parameters partition scheme in satellite geodesy, which is directly applicable to both the simplified yet common case where local parameters are unique to a single observation group and the generalized case where local parameters are shared by different groups of observations. Moreover, the Monte-Carlo VCE (MCVCE) algorithm, based on the stochastic trace estimation technique, is further extended in this paper to the generalized case. Two numerical simulation cases are investigated for gravity field model recovery to evaluate both the accuracy and efficiency of the proposed algorithm and the extended MCVCE algorithm in terms of trace calculation. Compared to the conventional algorithm, the relative trace calculation errors in the efficient algorithm are all negligibly below 10–7%, while in the MCVCE algorithm they can vary from 0.6 to 37% depending on the number of adopted random vector realizations and the specific applications. The efficient algorithm can achieve computational time reduction rates above 96% compared to the conventional algorithm for all gravity field model sizes considered in the paper. In the MCVCE algorithm, however, the time reduction rates can change from 61 to 99% for different implementations. Numéro de notice : A2022-186 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01599-9 Date de publication en ligne : 16/02/2022 En ligne : https://doi.org/10.1007/s00190-022-01599-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99907 [article]

Generating GPS decoupled clock products for precise point positioning with ambiguity resolution / Shuai Liu in Journal of geodesy, vol 96 n° 1 (January 2022)  

Permalink

Metalearning / Pavel Brazdil (2022)  

Permalink

Unsupervised generative models for data analysis and explainable artificial intelligence / Mohanad Abukmeil (2022)  

Permalink

Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images / Chen Zheng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)  

Permalink

The spatiotemporal implications of urbanization for urban heat islands in Beijing: A predictive approach based on CA–Markov modeling (2004–2050) / Muhammad Amir Siddique in Remote sensing, vol 13 n° 22 (November-2 2021)  

Permalink

Geoid determination through the combined least-squares adjustment of GNSS/levelling/gravity networks – a case study in Linyi, China / Dongmei Guo in Survey review, Vol 53 n° 381 (November 2021)  

Permalink

Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior / Maximilian Alexander Coenen in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

Permalink

A topic model based framework for identifying the distribution of demand for relief supplies using social media data / Ting Zhang in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Permalink

Towards the empirical determination of correlations in terrestrial laser scanner range observations and the comparison of the correlation structure of different scanners / Berit Schmitz in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)  

Permalink

Adaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Permalink