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Seasonal pattern in time series of variances of GPS residual errors Anova estimates / Darko Anđić in Geodetski vestnik, vol 63 n° 2 (June - August 2019)
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Titre : Seasonal pattern in time series of variances of GPS residual errors Anova estimates Type de document : Article/Communication Auteurs : Darko Anđić, Auteur Année de publication : 2019 Article en page(s) : pp 260 - 271 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] double différence
[Termes IGN] positionnement par GPS
[Termes IGN] propagation ionosphérique
[Termes IGN] propagation troposphérique
[Termes IGN] rayonnement solaire
[Termes IGN] résidu
[Termes IGN] série temporelle
[Termes IGN] variance
[Termes IGN] variation saisonnièreRésumé : (auteur) In this paper, which represents a continuation of the previous author's work, an inconstancy of GPS residual error ANOVA estimates and their variances are presented. For the purpose of the analysis, fixed solutions for all of the three coordinates, e (eastwards), n (northwards) and u (upwards), obtained by using ionosphere-free (L0) linear combination of double-difference phase observations in the processing of GPS data, were employed. The aim of the research was to consider the behaviour of variances of GPS residual error ANOVA estimates in time because there has not been any paper dealing with that issue so far. Herein, it turned out a seasonal pattern in related time series was present. In addition, it was concluded there was a difference in ANOVA estimate extreme values obtained when one considered daily data subsets compared to those obtained in the approach considering monthly data of the fixed solutions. GPS data collected at ending stations of a baseline of 40 km in length within a four-year period, involving the lowest and increased solar activity, were used in calculations. Numéro de notice : A2019-405 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2019.02.260-271 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.02.260-271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93509
in Geodetski vestnik > vol 63 n° 2 (June - August 2019) . - pp 260 - 271[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2019021 RAB Revue Centre de documentation En réserve L003 Disponible Journées de la recherche 2019 / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)
[article]
Titre : Journées de la recherche 2019 Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2019 Article en page(s) : pp 23 - 34 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Information géographique
[Termes IGN] apprentissage profond
[Termes IGN] base de connaissances
[Termes IGN] carte de Cassini
[Termes IGN] données localisées
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] géoréférencement
[Termes IGN] parcelle agricole
[Termes IGN] paroisse
[Termes IGN] photographie argentique
[Termes IGN] qualité des données
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] série temporelleRésumé : (Auteur) Cette année, les journées de la recherche de l’IGN ont fait la part belle aux réseaux de neurones – un sujet décidément très à la mode – ainsi qu’à différentes initiatives d’archivage et de consultation des données géographiques anciennes. Numéro de notice : A2019-308 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE/POSITIONNEMENT Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93284
in Géomatique expert > n° 127 (avril - mai 2019) . - pp 23 - 34[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P002141 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Analysis of ocean tide loading displacements by GPS kinematic precise point positioning: a case study at the China coastal site SHAO / H. Zhao in Survey review, vol 51 n° 365 (March 2019)
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Titre : Analysis of ocean tide loading displacements by GPS kinematic precise point positioning: a case study at the China coastal site SHAO Type de document : Article/Communication Auteurs : H. Zhao, Auteur ; Q. Zhang, Auteur ; R. Tu, Auteur ; Z. Liu, Auteur Année de publication : 2019 Article en page(s) : pp 172 - 182 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] analyse spectrale
[Termes IGN] Chine
[Termes IGN] données GPS
[Termes IGN] données marégraphiques
[Termes IGN] GPS en mode cinématique
[Termes IGN] littoral
[Termes IGN] marée océanique
[Termes IGN] positionnement ponctuel précis
[Termes IGN] série temporelle
[Termes IGN] surcharge océaniqueRésumé : (Auteur) Ocean tide loading (OTL) displacement amplitudes and phase lags of SHAO site are estimated by global positioning system (GPS), kinematic precise point positioning (PPP) and spectral analysis using 19 years of continuous GPS observations. In kinematic PPP, the 66 additional harmonic displacement parameters are replaced by the three time-varying displacement parameters without a priori modelled OTL displacements. By comparing the results with predictions from hybrid regional/global models, we are able to demonstrate that GPS/model agreements are at the level of 0.2 mm (horizontal) and 0.6 mm (vertical) for the four lunar constituents, 0.4 mm (horizontal) and 1.35 mm (vertical) for the four solar/sidereal constituents, and 0.2 mm (horizontal) and 0.3 mm (vertical) for the three long-period constituents. Finally, we conclude that GPS-derived lunar constituents can substitute for the model corrections in GPS data processing and the accuracy of GPS-derived solar/sidereal constituents needs to be improved by further studies. Numéro de notice : A2019-190 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2017.1407392 Date de publication en ligne : 30/11/2017 En ligne : https://doi.org/10.1080/00396265.2017.1407392 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92634
in Survey review > vol 51 n° 365 (March 2019) . - pp 172 - 182[article]A comparative study between least square and total least square methods for time-series analysis and quality control of sea level observations / Mahmoud Pirooznia in Marine geodesy, vol 42 n° 2 (March 2019)
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Titre : A comparative study between least square and total least square methods for time-series analysis and quality control of sea level observations Type de document : Article/Communication Auteurs : Mahmoud Pirooznia, Auteur ; Mehdi Raoofian Naeeni, Auteur ; Yazdan Amerian, Auteur Année de publication : 2019 Article en page(s) : pp 104 - 129 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] analyse de variance
[Termes IGN] contrôle qualité
[Termes IGN] données marégraphiques
[Termes IGN] filtrage du bruit
[Termes IGN] marégraphe
[Termes IGN] méthode des moindres carrés
[Termes IGN] niveau de la mer
[Termes IGN] série temporelle
[Termes IGN] valeur aberranteRésumé : (Auteur) In this study, the quality control of tide gauge observations is investigated by two methods of least square (LS-HE) and total least square harmonic estimation (TLS-HE). Particularly, it is shown how to deal with unexpected anomalies, including outliers, offset and gap in the time series of sea level height. To do so, at first the time series is constructed and then a method based on variance threshold is used to eliminate the possible outliers in the observations. Subsequently, a noise assessment algorithm is implemented and the signal is processed to find the possible times of offsets and to eliminate their corresponding observations from the time series. Finally, the signal is checked to find the periods of gap within the time series and then the gap area is predicted with correct observations. Gap filling analysis is performed in two contexts. In the first, only the significant frequencies of tide are considered in the modelling procedure, while in the second, all possible frequencies according to the period of observations are included. Our results show that for modelling and gap filling, the TLS-HE method has a better performance in a comparison with LS-HE method. Although, for offset and outlier detections, the LS-HE is recommended. It also indicates that the TLS-HE method provides a regular solution for gap filling analysis while LS-HE method needs a regularization scheme for which LSQR regularization is used. Numéro de notice : A2019-176 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2018.1553806 Date de publication en ligne : 24/01/2019 En ligne : https://doi.org/10.1080/01490419.2018.1553806 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92644
in Marine geodesy > vol 42 n° 2 (March 2019) . - pp 104 - 129[article]DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn / Roberto Interdonato in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)
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Titre : DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn Type de document : Article/Communication Auteurs : Roberto Interdonato, Auteur ; Dino Ienco, Auteur ; Raffaele Gaetano, Auteur ; Kenji Ose, Auteur Année de publication : 2019 Article en page(s) : pp 91 - 104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] image à haute résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] réseau neuronal convolutif
[Termes IGN] série temporelleRésumé : (Auteur) Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10 m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has been proved to be beneficial in the context of Land Use/Land Cover (LULC) map generation, unfortunately, most of machine learning approaches commonly leveraged in remote sensing field fail to take advantage of spatio-temporal dependencies present in such data. Recently, new generation deep learning methods allowed to significantly advance research in this field. These approaches have generally focused on a single type of neural network, i.e., Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), which model different but complementary information: spatial autocorrelation (CNNs) and temporal dependencies (RNNs). In this work, we propose the first deep learning architecture for the analysis of SITS data, namely DuPLO (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity. Our hypothesis is that, since CNNs and RNNs capture different aspects of the data, a combination of both models would produce a more diverse and complete representation of the information for the underlying land cover classification task. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Gard site in Mainland France and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal. Numéro de notice : A2019-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.011 Date de publication en ligne : 24/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92441
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 91 - 104[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Temporal and spatial high-resolution climate data from 1961 to 2100 for the German National Forest Inventory (NFI) / Helge Dietrich in Annals of Forest Science, vol 76 n° 1 (March 2019)PermalinkCombined orbits and clocks from IGS second reprocessing / Jake Griffiths in Journal of geodesy, vol 93 n° 2 (February 2019)PermalinkAnalyse de la déformation récente dans le Grand Tunis par interférométrie radar SAR / Anis Chaabani (2019)PermalinkAssessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data / Long Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkChallenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)PermalinkPermalinkDataPink, l'IA au service de l'information géographique / Anonyme in Géomatique expert, n° 126 (janvier - février 2019)PermalinkDPOD2014 : A new DORIS extension of ITRF2014 for precise orbit determination / Guilhem Moreaux in Advances in space research, vol 63 n° 1 (1 January 2019)PermalinkÉvaluation de la dégradation des forêts primaires par télédétection dans un espace de front pionnier consolidé d’Amazonie orientale (Paragominas) / Ali Fadhil Hasan (2019)PermalinkEvaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)PermalinkExploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau / Filsa Bioresita (2019)PermalinkMéthodes d'exploitation de données historiques pour la production de cartes d'occupation des sols à partir d'images de télédétection et en absence de données de référence de la période à cartographier / Benjamin Tardy (2019)PermalinkMicrowave indices from active and passive sensors for remote sensing applications / Emanuele Santi (2019)PermalinkMultimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)PermalinkPermalinkPermalinkTime-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series / Vivien Sainte Fare Garnot (2019)PermalinkUrban deformation monitoring using persistent scatterer Interferometry and SAR tomography / Michele Crosetto (2019)PermalinkUrban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series / Arnaud Le Bris (2019)PermalinkIdentification and extraction of seasonal geodetic signals due to surface load variations / Stacy Larochelle in Journal of geophysical research : Solid Earth, vol 123 n° 12 (December 2018)Permalink