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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6467 - 6479 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 bruit Résumé : (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 [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)  

Public [article]  inJournal of geodesy > vol 94 n° 6 (June 2020) 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] trajectoire Résumé : (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 [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)  

Public [article]  inMarine geodesy > Vol 43 n° 2 (March 2020) . - pp 134 - 162 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 hydrodynamique Résumé : (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 [article]

Geographies of maritime transport, Ch. 4. Geography versus topology in the evolution of the global container shipping network (1977-2016) / César Ducruet (2020)    

Public Titre de série : Geographies of maritime transport, Ch. 4 Titre : Geography versus topology in the evolution of the global container shipping network (1977-2016) Type de document : Chapitre/Contribution Auteurs : César Ducruet, Auteur ; Justin Berli, Auteur ; Mattia Bunel , Auteur Editeur : Camberley [Royaume Uni] : Edward Elgar Publishing Année de publication : 2020 Importance : pp 49 - 70 Note générale : bibliographie preprint dans HAL https://hal.archives-ouvertes.fr/halshs-02544851 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] navire [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] théorie des graphes [Termes descripteurs IGN] transport maritime Résumé : (Auteur) The dynamical properties of so-called spatial and complex networks are often overlooked in graph theory and network science in general. Container shipping provides a rare example of a global transport network that went through tremendous technological and geographic changes in the last decades or so. This chapter proposes for the first time an empirical analysis of no less than 40 years of inter-port vessel movement data (1977-2016) to describe the evolving properties of the global container shipping network. Main results confirm a number of stylized facts such as the growing size, connectivity, and centralization of this network due to several factors such as economies of scale in liner shipping and the rationalization of related maritime services, the emergence of hub ports, etc. We also provide a new cartography of how had the global container shipping network been geographically distributed over time, thereby highlighting major shifts in terms of port hierarchies and main corridors. We believe that this chapter will contribute to a better understanding of the complex linkages between network structure, technological change, and spatial change, opening the way for new research paths on maritime transport research and network science in general when focusing on evolutionary dynamics. Numéro de notice : H2020-002 Affiliation des auteurs : UMR LaSTIG+Ext Thématique : GEOMATIQUE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.4337/9781788976640.00008 date de publication en ligne : 16/04/2020 En ligne : https://doi.org/10.4337/9781788976640.00008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95084

Documents numériques

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Ship 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)  

Public [article]  inRemote sensing > Vol 11 n° 24 (December-2 2019) . - n° 2997 Titre : Ship identification and characterization in Sentinel-1 SAR images with multi-task deep learning Type de document : Article/Communication Auteurs : Clément Dechesne , Auteur ; Sébastien Lefèvre, Auteur ; Rodolphe Vadaine, Auteur ; Guillaume Hajduch, Auteur ; Ronan Fablet, Auteur Année de publication : 2019 Projets : SESAME / Fablet, Ronan Article en page(s) : n° 2997 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] cible mobile [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] navire Résumé : (auteur) The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m). Numéro de notice : A2019-632 Affiliation des auteurs : UMR LaSTIG+Ext Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11242997 date de publication en ligne : 13/12/2019 En ligne : https://doi.org/10.3390/rs11242997 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95325 [article]

Discriminating 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)  

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Ship Traffic Simulation to assess noise exposure of whale populations in Canada / Axel Orger (2017) 

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The 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)  

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Trajectoires d’objets mobiles dans un espace support fixe / Elodie Buard in Revue internationale de géomatique, vol 25 n° 3 (septembre - novembre 2015)  

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The 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)

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Visualization, statistical analysis, and mining of historical vessel data / Sabarish Senthilnathan Muthu (2015)  

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Improvement 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)  

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Study on the new methods of ship object detection based on GNSS reflection / Yong Lu in Marine geodesy, vol 36 n° 1 (January - March 2013)  

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Encyclopédie des tours du monde / C. Nau (2012)

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Détection de bateaux dans les images satellitaires optiques panchromatiques / N. Proia in Revue Française de Photogrammétrie et de Télédétection, n° 194 (Mai 2011)

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