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Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC4A / Pierre Bosser in Earth System Science Data, vol 13 n° inconnu ([01/01/2021])
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Titre : Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC4A Type de document : Article/Communication Auteurs : Pierre Bosser , Auteur ; Olivier Bock
, Auteur ; Cyrille Flamant, Auteur ; Sandrine Bony, Auteur ; Sabrina Speich, Auteur
Année de publication : 2021 Projets : 3-projet - voir note / Note générale : bibliographie
projets GEMMOC and VEGAN du CNRS program LEFE/INSU
Both the raw GNSS measurements and the IWV estimates are available through the AERIS data center (https://en.aeris-data.fr/). The digital object identifiers (DOIs) for R/V Atalante IWV and raw datasets are https://doi.org/10.25326/71 (Bosser et al., 2020a) and https://doi.org/10.25326/74 (Bosser et al., 2020d), respectively. The DOIs for the R/V Maria S. Merian IWV and raw datasets are https://doi.org/10.25326/72 (Bosser et al., 2020b) and https://doi.org/10.25326/75 (Bosser et al., 2020e), respectively. The DOIs for the R/V Meteor IWV and raw datasets are https://doi.org/10.25326/73 (Bosser et al., 2020c) and https://doi.org/10.25326/76 (Bosser et al., 2020f), respectively.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes descripteurs IGN] analyse comparative
[Termes descripteurs IGN] coordonnées GNSS
[Termes descripteurs IGN] données météorologiques
[Termes descripteurs IGN] erreur systématique
[Termes descripteurs IGN] navire
[Termes descripteurs IGN] station permanente
[Termes descripteurs IGN] teneur intégrée en vapeur d'eauRésumé : (auteur) In the framework of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) campaign that took place in January and February 2020, integrated water vapour (IWV) contents were retrieved over the open Tropical Atlantic Ocean using Global Navigation Satellite Systems (GNSS) data acquired from three research vessels (R/Vs): R/V Atalante, R/V Maria S. Merian, and R/V Meteor. This paper describes the GNSS processing method and compares the GNSS IWV retrievals with IWV estimates from the European Center for Medium-range Weather Forecast (ECMWF) fifth ReAnalysis (ERA5), from the Moderate-Resolution Imaging Spectroradiometer (MODIS) infra-red products, and from terrestrial GNSS stations located along the tracks of the ships. The ship-borne GNSS IWVs retrievals from R/V Atalante and R/V Meteor compare well with ERA5, with small biases (−1.62 kg m−2 for R/V Atalante and +0.65 kg m−2 for R/V Meteor) and a root mean square (RMS) difference about 2.3 kg m−2. The results for the R/V Maria S. Merian are found to be of poorer quality, with RMS difference of 6 kg m−2 which are very likely due to the location of the GNSS antenna on this R/V prone to multipath effects. The comparisons with ground-based GNSS data confirm these results. The comparisons of all three R/V IWV retrievals with MODIS infrared product show large RMS differences of 5–7 kg m−2, reflecting the enhanced uncertainties of this satellite product in the tropics. These ship-borne IWV retrievals are intended to be used for the description and understanding of meteorological phenomena that occurred during the campaign, east of Barbados, Guyana and northern Brazil. Numéro de notice : A2021-064 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-2020-282 En ligne : https://doi.org/10.5194/essd-2020-282 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96840
in Earth System Science Data > vol 13 n° inconnu [01/01/2021][article]Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination / Frederik Hass in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)
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Titre : Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination Type de document : Article/Communication Auteurs : Frederik Hass, Auteur ; Jamal Jokar Arsanjani, Auteur Année de publication : 2020 Article en page(s) : n° 758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] Groenland
[Termes descripteurs IGN] hydrocarbure
[Termes descripteurs IGN] iceberg
[Termes descripteurs IGN] image radar moirée
[Termes descripteurs IGN] image Sentinel-SAR
[Termes descripteurs IGN] navire
[Termes descripteurs IGN] océan
[Termes descripteurs IGN] seuillage d'image
[Termes descripteurs IGN] trafic maritimeRésumé : (auteur) Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data. Numéro de notice : A2020-808 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9120758 date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.3390/ijgi9120758 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96953
in ISPRS International journal of geo-information > vol 9 n° 12 (December 2020) . - n° 758[article]Ship detection in SAR images via local contrast of Fisher vectors / Xueqian Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
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Titre : Ship detection in SAR images via local contrast of Fisher vectors Type de document : Article/Communication Auteurs : Xueqian Wang, Auteur ; Gang Li, Auteur ; Xiao-Ping Zhang, Auteur ; You He, Auteur Année de publication : 2020 Article en page(s) : pp 6467 - 6479 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes descripteurs IGN] algorithme de superpixels
[Termes descripteurs IGN] contraste local
[Termes descripteurs IGN] détection d'objet
[Termes descripteurs IGN] détection de cible
[Termes descripteurs IGN] distribution de Fisher
[Termes descripteurs IGN] fouillis d'échos
[Termes descripteurs IGN] image radar moirée
[Termes descripteurs IGN] navire
[Termes descripteurs IGN] processus gaussien
[Termes descripteurs IGN] rapport signal sur bruitRésumé : (auteur) Existing superpixel-based detection algorithms for ship targets in synthetic aperture radar (SAR) images are often derived from the local contrast of intensities (i.e., the local contrast of the first-order information of superpixels) leading to deteriorating performance in low signal-to-clutter ratio (SCR) cases due to the low contrast between the intensities of targets and the clutter. In this article, we propose a new superpixel-based detector to improve the performance of ship target detection in SAR images via the local contrast of fisher vectors (LCFVs). The new LCFV-based detector exploits multiorder features of the superpixels based on the Gaussian mixture model (GMM) and accordingly improves the discrimination capability between the ship targets and the sea clutter, especially in low SCR cases. Experimental results demonstrate that the proposed LCFV-based detection algorithm provides better detection performance than the commonly used detection algorithms. Numéro de notice : A2020-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976880 date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976880 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95713
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6467 - 6479[article]Improving GNSS-acoustic positioning by optimizing the ship’s track lines and observation combinations / Guanxu Chen in Journal of geodesy, vol 94 n° 6 (June 2020)
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Titre : Improving GNSS-acoustic positioning by optimizing the ship’s track lines and observation combinations Type de document : Article/Communication Auteurs : Guanxu Chen, Auteur ; Yang Liu, Auteur ; Yanxiong Liu, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes descripteurs IGN] contrainte géométrique
[Termes descripteurs IGN] fond marin
[Termes descripteurs IGN] GNSS-Acoustique
[Termes descripteurs IGN] navire
[Termes descripteurs IGN] positionnement par GNSS
[Termes descripteurs IGN] précision du positionnement
[Termes descripteurs IGN] profondeur
[Termes descripteurs IGN] station GNSS
[Termes descripteurs IGN] trajectoireRésumé : (auteur) The position of a seafloor geodetic station can be determined by combining Global Navigation Satellite System (GNSS) and acoustic technologies, called GNSS-acoustic positioning. The precision of GNSS-acoustic positioning, a technique that employs the distance intersection, is determined by the positioning geometry formed by the ship’s track lines with respect to the seafloor station and the errors in the measurements. In the context of a shallow sea trial, we studied three key techniques in GNSS-acoustic positioning: the optimal geometric configuration, differencing techniques for acoustic observations and depth constraints offered by pressure gauges. The results showed that the optimal geometric configuration is a circular track with a radius of 2‾√ times the depth plus an overhead cross-track with a length of the circle diameter. Differenced observations can improve the horizontal positioning precision but will worsen the vertical positioning precision due to the change in the geometric configuration and the elimination of vertical information if the number of observations is limited. The proposed difference strategy, that is, applying a symmetric location difference operator to the circular track and an undifference operator to the cross-track, can effectively improve the horizontal precision and avoid vertical defects. By using relative depth observations from two pressure gauges as constraints, the vertical defects of GNSS-acoustic positioning can be improved, achieving a better vertical positioning precision. Applying the proposed methods to high-quality GNSS and acoustic observations, the positioning precision of a shallow seafloor geodetic station can be better than 2 cm. Numéro de notice : A2020-377 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern date de publication en ligne : 27/06/2020 En ligne : https://doi.org/10.1007/s00190-020-01389-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95369
in Journal of geodesy > vol 94 n° 6 (June 2020)[article]Validation of marine geoid models by utilizing hydrodynamic model and shipborne GNSS profiles / Sander Varbla in Marine geodesy, Vol 43 n° 2 (March 2020)
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Titre : Validation of marine geoid models by utilizing hydrodynamic model and shipborne GNSS profiles Type de document : Article/Communication Auteurs : Sander Varbla, Auteur ; Artu Ellmann, Auteur ; Nicole Delpeche-Ellmann, Auteur Année de publication : 2020 Article en page(s) : pp 134 - 162 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes descripteurs IGN] Baltique, mer
[Termes descripteurs IGN] données marégraphiques
[Termes descripteurs IGN] force de gravitation
[Termes descripteurs IGN] geoïde marin
[Termes descripteurs IGN] instrument embarqué
[Termes descripteurs IGN] instrumentation GNSS
[Termes descripteurs IGN] levé gravimétrique
[Termes descripteurs IGN] navire
[Termes descripteurs IGN] niveau de la mer
[Termes descripteurs IGN] simulation hydrodynamiqueRésumé : (auteur) An essential role of the FAMOS international cooperation project is to obtain new marine gravity observations over the Baltic Sea for improving gravimetric geoid modelling. To achieve targeted 5 cm modelling accuracy, it is important to acquire new gravimetric data, as the existing data over some regions are inaccurate and sparse. As the accuracy of contemporary geoid models over marine areas remains unknown, it is important to evaluate geoid modelling outcome by independent data. Thus, this study presents results of a shipborne marine gravity and GNSS campaign for validation of existing geoid models conducted in the eastern section of the Baltic Sea. Challenging aspects for utilizing shipborne GNSS profiles tend to be with quantifying vessel’s attitude, processing of noise in the data and referencing to the required datum. Consequently, the novelty of this study is in the development of methodology that considers the above-mentioned challenges. In addition, tide gauge records in conjunction with an operational hydrodynamic model are used to identify offshore sea level dynamics during the marine measurements. The results show improvements in geoid modelling due to new marine gravimetric data. It is concluded that the marine GNSS profiles can potentially provide complementary constraints in problematic geoid modelling areas. Numéro de notice : A2020-051 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2019.1701153 date de publication en ligne : 20/01/2020 En ligne : https://doi.org/10.1080/01490419.2019.1701153 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94918
in Marine geodesy > Vol 43 n° 2 (March 2020) . - pp 134 - 162[article]Geographies of maritime transport, Ch. 4. Geography versus topology in the evolution of the global container shipping network (1977-2016) / César Ducruet (2020)
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PermalinkIWV retrieval from shipborne GPS receiver on hydrographic ship Borda [diaporama] / Olivier Bock (2020)
PermalinkShip identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)
PermalinkDiscriminating ship from radio frequency interference based on noncircularity and non-gaussianity in sentinel-1 SAR imagery / Xiangguang Leng in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)
PermalinkPermalinkThe Costa Concordia last cruise: The first application of high frequency monitoring based on COSMO-SkyMed constellation for wreck removal / Andrea Ciampalini in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)
PermalinkTrajectoires d’objets mobiles dans un espace support fixe / Elodie Buard in Revue internationale de géomatique, vol 25 n° 3 (septembre - novembre 2015)
PermalinkThe use of the terrestrial photogrammetry in reverse engineering applications / Naci Yastikli in International journal of 3-D information modeling, vol 4 n° 2 (April - June 2015)
PermalinkVisualization, statistical analysis, and mining of historical vessel data / Sabarish Senthilnathan Muthu (2015)
PermalinkImprovement of GPS/acoustic seafloor positioning precision through controlling the ship’s track line / M. Sato in Journal of geodesy, vol 87 n° 9 (September 2013)
PermalinkStudy on the new methods of ship object detection based on GNSS reflection / Yong Lu in Marine geodesy, vol 36 n° 1 (January - March 2013)
PermalinkPermalinkDétection de bateaux dans les images satellitaires optiques panchromatiques / N. Proia in Revue Française de Photogrammétrie et de Télédétection, n° 194 (Mai 2011)
PermalinkPermalinkAn integrated approach for visual analysis of a multisource moving objects knowledge base / N. Wllems in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
PermalinkSpace-time density of trajectories : exploring spatio-temporal patterns in movement data / Urška Demšar in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
PermalinkSpatio-temporal trajectory analysis of mobile objects following the same itinerary / L. Etienne (26/05/2010)
PermalinkA scheme for ship detection in inhomogeneous regions based on segmentation of SAR images / F. Zhang in International Journal of Remote Sensing IJRS, vol 29 n°19-20 (October 2008)
PermalinkA new application for PolSAR imagery in the field of moving target indication/ship detection / C. Liu in IEEE Transactions on geoscience and remote sensing, vol 45 n° 11 Tome 1 (November 2007)
PermalinkMaritime GIS-applications entering the offshore world: accurate information vital in case of large vessels / R. Wevers in Geoinformatics, vol 8 n° 8 (01/12/2005)
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