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Modelling of the timeseries of GNSS coordinates and their interaction with average magnitude earthquakes / Sanja Tucikesic in Geodetski vestnik, Vol 63 n° 4 (December 2019)
[article]
Titre : Modelling of the timeseries of GNSS coordinates and their interaction with average magnitude earthquakes Type de document : Article/Communication Auteurs : Sanja Tucikesic, Auteur ; Dragan Blagojevic, Auteur Année de publication : 2019 Article en page(s) : pp 525 - 540 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse diachronique
[Termes IGN] analyse spectrale
[Termes IGN] Bosnie-Herzégovine
[Termes IGN] bruit (théorie du signal)
[Termes IGN] bruit blanc
[Termes IGN] compensation par moindres carrés
[Termes IGN] coordonnées GNSS
[Termes IGN] déformation de la croute terrestre
[Termes IGN] modèle stochastique
[Termes IGN] séisme
[Termes IGN] Serbie
[Termes IGN] série temporelle
[Termes IGN] station GNSS
[Termes IGN] variation temporelleRésumé : (auteur) In this article the time series data of GNSS station coordinates are analysed, using least-squares spectral analysis (LSSA). One type of LSSA, the method of estimating a frequency spectrum, is the Lomb–Scargle method. Because of the presence of discontinuities in GNSS measurements, we applied Lomb–Scargle model for detecting and characterizing periodicity. We analyzed time series data from the station SRJV (Sarajevo), for a period of about 20 years, and BEOG (Belgrade), for a period of about 5 years. The spectral analysis is used to determine quickly the predominant noise in the position time series. Analyzed spectral indices of noise (α) of GNSS coordinate time series of SRJV and BEOG are in the range of -1 Numéro de notice : A2019-579 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2019.04.525-540 Date de publication en ligne : 24/05/2019 En ligne : https://doi.org/10.15292/geodetski-vestnik.2019.04.525-540 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94467
in Geodetski vestnik > Vol 63 n° 4 (December 2019) . - pp 525 - 540[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2019041 RAB Revue Centre de documentation En réserve L003 Disponible Addressing overfitting on point cloud classification using Atrous XCRF / Hasan Asy’ari Arief in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)
[article]
Titre : Addressing overfitting on point cloud classification using Atrous XCRF Type de document : Article/Communication Auteurs : Hasan Asy’ari Arief, Auteur ; Ulf Geir Indahl, Auteur ; Geir-Harald Strand, Auteur ; Håvard Tveite, Auteur Année de publication : 2019 Article en page(s) : pp 90 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification automatique
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal profond
[Termes IGN] semis de pointsRésumé : (Auteur) Advances in techniques for automated classification of point cloud data introduce great opportunities for many new and existing applications. However, with a limited number of labelled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 85.0% in term of overall accuracy, and 71.1% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score. Additionally, transfer learning using the Bergen 2018 dataset, without model retraining, was also performed. Even though our proposal provides a consistent 3% improvement in term of accuracy, more work still needs to be done to alleviate the generalization problem on the domain adaptation and the transfer learning field. Numéro de notice : A2019-312 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.002 Date de publication en ligne : 11/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93337
in ISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 90 - 101[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners / Tomislav Medic in Journal of applied geodesy, vol 13 n° 3 (July 2019)
[article]
Titre : Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners Type de document : Article/Communication Auteurs : Tomislav Medic, Auteur ; Christoph Holst, Auteur ; Jannik Janssen, Auteur ; Heiner Kuhlmann, Auteur Année de publication : 2019 Article en page(s) : pp 179 – 197 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] centroïde
[Termes IGN] compensation par moindres carrés
[Termes IGN] détection de cible
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage d'instrument
[Termes IGN] incertitude de mesurage
[Termes IGN] métrologie dimensionelle
[Termes IGN] modèle stochastique
[Termes IGN] semis de points
[Termes IGN] télémètre laser terrestreRésumé : (auteur) The target-based point cloud registration and calibration of terrestrial laser scanners (TLSs) are mathematically modeled and solved by the least-squares adjustment. However, usual stochastic models are simplified to a large amount: They generally employ a single point measurement uncertainty based on the manufacturers’ specifications. This definition does not hold true for the target-based calibration and registration due to the fact that the target centroid is derived from multiple measurements and its uncertainty depends on the detection procedure as well. In this study, we empirically investigate the precision of the target centroid detection and define an empirical stochastic model in the form of look-up tables. Furthermore, we compare the usual stochastic model with the empirical stochastic model on several point cloud registration and TLS calibration experiments. There, we prove that the values of usual stochastic models are underestimated and incorrect, which can lead to multiple adverse effects such as biased results of the estimation procedures, a false a posteriori variance component analysis, false statistical testing, and false network design conclusions. In the end, we prove that some of the adverse effects can be mitigated by employing the a priori knowledge about the target centroid uncertainty behavior. Numéro de notice : A2019-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0032 Date de publication en ligne : 22/03/2019 En ligne : https://doi.org/10.1515/jag-2018-0032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93119
in Journal of applied geodesy > vol 13 n° 3 (July 2019) . - pp 179 – 197[article]Influence of stochastic modeling for inter-system biases on multi-GNSS undifferenced and uncombined precise point positioning / Feng Zhou in GPS solutions, vol 23 n° 3 (July 2019)
[article]
Titre : Influence of stochastic modeling for inter-system biases on multi-GNSS undifferenced and uncombined precise point positioning Type de document : Article/Communication Auteurs : Feng Zhou, Auteur ; Danan Dong, Auteur ; Xin Li, Auteur ; Harald Schuh, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] bruit blanc
[Termes IGN] décalage d'horloge
[Termes IGN] erreur systématique inter-systèmes
[Termes IGN] estimation statistique
[Termes IGN] modèle stochastique
[Termes IGN] positionnement par GNSS
[Termes IGN] positionnement ponctuel précisRésumé : (auteur) The focus of this study is on proper modeling of the dynamics for inter-system biases (ISBs) in multi-constellation Global Navigation Satellite System (GNSS) precise point positioning (PPP) processing. First, the theoretical derivation demonstrates that the ISBs originate from not only the receiver-dependent hardware delay differences among different GNSSs but also the receiver-independent time differences caused by the different clock datum constraints among different GNSS satellite clock products. Afterward, a comprehensive evaluation of the influence of ISB stochastic modeling on undifferenced and uncombined PPP performance is conducted, i.e., random constant, random walk process, and white noise process are considered. We use data based on a 1-month period (September 2017) Multi-GNSS Experiment (MGEX) precise orbit and clock products from four analysis centers (CODE, GFZ, CNES, and WHU) and 160 MGEX tracking stations. The results demonstrate that generally, the positioning performance of PPP in terms of convergence time and positioning accuracy with the final products from CODE, CNES, and WHU is comparable among the three ISB handling schemes. However, estimating ISBs as random walk process or white noise process outperforms that as the random constant when using the GFZ products. These results indicate that the traditional estimation of ISBs as the random constant may not always be reasonable in multi-GNSS PPP processing. To achieve more reliable positioning results, it is highly recommended to consider the ISBs as random walk process or white noise process in multi-GNSS PPP processing. Numéro de notice : A2019-199 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0852-0 Date de publication en ligne : 09/04/2019 En ligne : https://doi.org/10.1007/s10291-019-0852-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92654
in GPS solutions > vol 23 n° 3 (July 2019)[article]Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)
[article]
Titre : Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density Type de document : Article/Communication Auteurs : Yuan Li, Auteur ; Bo Wu, Auteur ; Xuming Ge, Auteur Année de publication : 2019 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification
[Termes IGN] classification basée sur les régions
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Hong-Kong
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] Paris (75)
[Termes IGN] scène urbaine
[Termes IGN] segmentation en régions
[Termes IGN] segmentation hiérarchique
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) Objects are formed by various structures and such structural information is essential for the identification of objects, especially for street facilities presented by mobile laser scanning (MLS) data with abundant details. However, due to the large volume of data, large variations in point density, noise and complexity of scanned scenes, the achievement of effective decomposition of objects into physical meaningful structures remains a challenge issue. And structural information has been rarely considered to improve the accuracy of distinguishing between objects with global or local similarity, such as traffic signs and traffic lights. Therefore, we propose a structural segmentation and classification method for MLS point clouds that is efficient and robust to variations in point density and complex urban scenes. During the segmentation stage, a novel region growing approach and a multi-size supervoxel segmentation algorithm robust to noise and varying density are combined to extract effective local shape descriptors. Structural components with physically meaningful labels are generated via structural labelling and clustering. During the classification stage, we consider the structural information at various scales and locations and encode it into a conditional random-field model for unary and pairwise inferences. High-order potentials are also introduced into the conditional random field to eliminate regional label noise. These high-order potentials are defined upon regions independent of connection relationships and can therefore take effect on isolated nodes. Experiments with two MLS datasets of typical urban scenes in Paris and Hong Kong were used to evaluate the performance of the proposed method. Nine and eleven different object classes were recognized from these two datasets with overall accuracies of 97.13% and 95.79%, respectively, indicating the effectiveness of the proposed method of interpreting complex urban scenes from point clouds with large variations in point density. Compared with previous studies on the Paris dataset, our method was able to recognize more classes and obtained a mean F1-score of 72.70% of seven common classes, being higher than the best of previous results. Numéro de notice : A2019-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.007 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93075
in ISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Semantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)PermalinkRefining ionospheric delay modeling for undifferenced and uncombined GNSS data processing / Qile Zhao in Journal of geodesy, vol 93 n° 4 (April 2019)PermalinkThe stochastic model for Global Navigation Satellite Systems and terrestrial laser scanning observations: A proposal to account for correlations in least squares adjustment / Gaël Kermarrec in Journal of applied geodesy, vol 13 n° 2 (April 2019)PermalinkConditional random field and deep feature learning for hyperspectral image classification / Fahim Irfan Alam in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkLand cover classification in combined elevation and optical images supported by OSM data, mixed-level features, and non-local optimization algorithms / Dimitri Bulatov in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)PermalinkCorrecting rural building annotations in OpenStreetMap using convolutional neural networks / John E. Vargas-Muñoz in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkPotentialités de l’imagerie couleur embarquée pour la détection et la cartographie des maladies fongiques de la vigne / Florent Abdelghafour (2019)PermalinkPermalinkAutomatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkDeep multi-task learning for a geographically-regularized semantic segmentation of aerial images / Michele Volpi in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)Permalink