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Automatic detection of thin oil films on water surfaces in ultraviolet imagery / Ming Xie in Photogrammetric record, vol 38 n° 181 (March 2023)
[article]
Titre : Automatic detection of thin oil films on water surfaces in ultraviolet imagery Type de document : Article/Communication Auteurs : Ming Xie, Auteur ; Xiurui Zhang, Auteur ; Ying Li, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 47 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] détection automatique
[Termes IGN] filtre optique
[Termes IGN] hydrocarbure
[Termes IGN] image AVIRIS
[Termes IGN] marée noire
[Termes IGN] niveau de gris (image)
[Termes IGN] rayonnement ultraviolet
[Termes IGN] segmentation d'image
[Termes IGN] seuillage binaire
[Termes IGN] surface de la merRésumé : (auteur) Among the various remote sensing technologies that have been applied to monitor oil spills on the sea surface, passive ultraviolet (UV) imaging is a controversial one that has raised some disputes in the community of oil spill remote sensing. As a result, the research and applications of oil spill detection using passive UV imaging have not been as developed as other methods. In order to clarify some existing questions on oil spill detection using passive UV remote sensing technology, this paper discusses the needs of thin oil film detection, examines the feasibility of thin oil film detection using passive UV imaging through field experiments under controlled conditions and validates it with the UV imagery derived from the airborne visible/infrared imaging spectrometer (AVIRIS) observation of the Deepwater Horizon oil spill. Two types of fully automatic models are designed to extract the thin oil films on the water surface: (1) a binary classification model based on an adaptive threshold; (2) an unsupervised image segmentation model based on image clustering and greyscale histogram analysis. The two models are tested on the UV imagery obtained through both field experiments and AVIRIS observations. The results indicate that the binary classification model can extract the thin oil films with reasonable accuracy under stable imaging conditions, while the unsupervised image clustering model can robustly detect the thin oil films at the cost of higher computational complexity. These results infer that passive UV imaging is an effective way to detect thin oil films and could be applied to provide early warning at the beginning stage of oil spills and reduce further damage. It may also be applied as a supplementary method for oil spill detection to achieve comprehensive oil spill monitoring. Numéro de notice : A2023-163 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12439 Date de publication en ligne : 09/02/2023 En ligne : https://doi.org/10.1111/phor.12439 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102866
in Photogrammetric record > vol 38 n° 181 (March 2023) . - pp 47 - 62[article]
Titre : Artificial intelligence oceanography Type de document : Monographie Auteurs : Xiaofeng Li, Éditeur scientifique ; Fan Wang, Éditeur scientifique Editeur : Springer Nature Année de publication : 2023 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-981-19637-5-9 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algue
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cyclone
[Termes IGN] détection d'objet
[Termes IGN] iceberg
[Termes IGN] intelligence artificielle
[Termes IGN] océanographie
[Termes IGN] température de surface de la merRésumé : (éditeur) This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing. Note de contenu : 1- Artificial Intelligence Foundation of smart ocean
2- Forecasting tropical instability waves based on artificial intelligence
3- Sea surface height anomaly prediction based on artificial intelligence
4- Satellite data-driven internal solitary wave forecast based on machine learning techniques
5- AI-based subsurface thermohaline structure retrieval from remote sensing observations
6- Ocean heat content retrieval from remote sensing data based on machine learning
7- Detecting tropical cyclogenesis using broad learning system from satellite passive microwave observations
8- Tropical cyclone monitoring based on geostationary satellite imagery
9- Reconstruction of pCO2 data in the Southern ocean based on feedforward neural network
10- Detection and analysis of mesoscale eddies based on deep learning
11- Deep convolutional neural networks-based coastal inundation mapping from SAR imagery: with one application case for Bangladesh, a UN-defined least developed country
12- Sea ice detection from SAR images based on deep fully convolutional networks
13- Detection and analysis of marine green algae based on artificial intelligence
14- Automatic waterline extraction of large-scale tidal flats from SAR images based on deep convolutional neural networks
15- Extracting ship’s size from SAR images by deep learning
16- Benthic organism detection, quantification and seamount biology detection based on deep learningNuméro de notice : 24105 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.1007/978-981-19-6375-9 En ligne : https://link.springer.com/book/10.1007/978-981-19-6375-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103058 Geographic-dependent variational parameter estimation: A case study with a 2D ocean temperature model / Zhenyang Du in Journal of Marine Systems, vol 237 (January 2023)
[article]
Titre : Geographic-dependent variational parameter estimation: A case study with a 2D ocean temperature model Type de document : Article/Communication Auteurs : Zhenyang Du, Auteur ; Xuefeng Zhang, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] assimilation des données
[Termes IGN] estimation statistique
[Termes IGN] modèle océanographique
[Termes IGN] océanographie spatiale
[Termes IGN] température de surface de la mer
[Termes IGN] teneur en chaleur de l'océanRésumé : (auteur) Using observational information to tune uncertain physical parameters in an ocean model via a robust data assimilation method has great potential to reduce model bias and improve the quality of sea temperature analysis and prediction. However, how observational information should be used to optimize geographic-dependent parameters through four-dimensional variational (4DVAR) data assimilation, which is one of the most prevailing assimilation methods, has not been fully studied. In this study, a two-step 4DVAR method is proposed to enhance parameter correction when the assimilation model contains biased geographic-dependent parameters within a biased model framework. Here, the biased parameters are set to an oceanic eddy diffusion coefficient, Kv, that plays an important role in modulating synoptic, seasonal and long-term changes in ocean heat content. Within a twin assimilation experiment framework, the temperature “observations” generated from sampling a “truth” model are assimilated into a biased model to investigate to what extent Kv can be estimated using the 4DVAR method when Kv remains geographic-dependent. The results show that the geographic-dependent Kv distribution can be optimally estimated to further improve the sea temperature analysis performance compared with the state estimation only method. In addition, the model prediction performance is also discussed with optimally estimated parameters under various conditions of noisy and/or sparse ocean observations. These results provide some insights for the prediction of ocean temperature mixing and stratification in a 3D primitive ocean numerical model using 4DVAR data assimilation. Numéro de notice : A2023-080 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jmarsys.2022.103824 En ligne : https://doi.org/10.1016/j.jmarsys.2022.103824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102716
in Journal of Marine Systems > vol 237 (January 2023)[article]Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)
[article]
Titre : Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami Type de document : Article/Communication Auteurs : Riantini Virtriana, Auteur ; Agung Budi Harto, Auteur ; Fiza Wira Atmaja, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 28 - 51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] base de données d'images
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] dommage matériel
[Termes IGN] données Copernicus
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Worldview
[Termes IGN] Indonésie
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation d'image
[Termes IGN] tsunamiRésumé : (auteur) In Indonesia, tsunamis are frequent events. In 2000–2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building damage. This study aimed to detect the building damage in the Labuan District, Banten Province. Machine learning methods were used to detect building damage using random forest with object-based techniques. No previous research has combined selected predictors into scenarios; hence, the novelty of this study is combining various random forest predictors to identify the extent of building damage using 14 predictor scenarios. In addition, field surveys were conducted two years and nine months after the tsunami to observe the changes and efforts made. The results of the random forest classification were validated and compared with three datasets, namely xBD, Copernicus, and field survey data. The results of this study can help classify the level of building damage using satellite imagery to improve mitigation in tsunami-prone areas. Numéro de notice : A2023-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/19475705.2022.2147455 Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1080/19475705.2022.2147455 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102307
in Geomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - pp 28 - 51[article]Modeling the gravitational effects of ocean tide loading at coastal stations in the China earthquake gravity network based on GOTL software / Chuandong Zhu in Journal of applied geodesy, vol 17 n° 1 (January 2023)
[article]
Titre : Modeling the gravitational effects of ocean tide loading at coastal stations in the China earthquake gravity network based on GOTL software Type de document : Article/Communication Auteurs : Chuandong Zhu, Auteur ; Liuqing Pang, Auteur ; Didi Sheng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 15 - 27 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] champ de pesanteur local
[Termes IGN] Chine
[Termes IGN] fonction de Green
[Termes IGN] littoral
[Termes IGN] marée océanique
[Termes IGN] modèle de géopotentiel
[Termes IGN] modèle numérique de surface
[Termes IGN] surcharge océaniqueRésumé : (auteur) The gravitational effects of ocean tide loading, which are one of the main factors affecting gravity measurements, consist of three components: (1) direct attraction from the tidal water masses, (2) radial displacement of the observing station due to the tidal load, and (3) internal redistribution of masses due to crustal deformation. In this study, software for gravitational effects of ocean tide loading was developed by evaluating a convolution integral between the ocean tide model and Green’s functions that describe the response of the Earth to tide loading. The effects of three-dimensional station coordinates, computational grid patterns, ocean tide models, Green’s functions, coastline, and local tide gauge were comprehensively considered in the programming process. Using a larger number of high-precision coastlines, ocean tide models, and Green’s functions, the reliability and applicability of the software were analyzed at coastal stations in the China Earthquake Gravity Network. The software can provide the amplitude and phase for ocean tide loading and produce a predicted gravity time series. The results can effectively reveal the variation characteristics of ocean tide loading in space and time. The computational gravitational effects of ocean tide loading were compared and analyzed for different ocean tide models and Green’s functions. The results show that different ocean tide models and Green’s functions have certain effects on the calculated values of loading gravity effects. Furthermore, a higher-precision local ocean tide model, digital elevation model, and local tidal gauge record can be further imported into our software to improve the accuracy of loading gravity effects in the global and local zones. The software is easy to operate and can provide a comprehensive platform for correcting the gravitational effects of ocean tide loading at stations in the China Earthquake Gravity Network. Numéro de notice : A2023-112 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2022-0023 Date de publication en ligne : 03/11/2022 En ligne : https://doi.org/10.1515/jag-2022-0023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102471
in Journal of applied geodesy > vol 17 n° 1 (January 2023) . - pp 15 - 27[article]Automatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery / Yuxin Wang in Science of the total environment, vol 853 (December 2022)PermalinkVertical deformation and residual altimeter systematic errors around continental Australia inferred from a Kalman-based approach / Mohammad-Hadi Rezvani in Journal of geodesy, vol 96 n° 12 (December 2022)PermalinkIntegrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability / Benjamin T. Gutierrez in Earth and space science, vol 9 n° 11 (November 2022)PermalinkTidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran / Kourosh Shahryari Nia in Marine geodesy, vol 45 n° 6 (November 2022)PermalinkUse of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand / Kiatkulchai Jitt-Aer in Natural Hazards, vol 113 n° 1 (August 2022)PermalinkLittoraux sous double surveillance / Laurent Polidori in Géomètre, n° 2204 (juillet-août 2022)PermalinkHow can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? / Katja Kuhwald in Remote sensing in ecology and conservation, vol 8 n° 3 (June 2022)PermalinkModeling gravimetric signatures of third-degree ocean tides and their detection in superconducting gravimeter records / Roman Sulzbach in Journal of geodesy, vol 96 n° 5 (May 2022)PermalinkLa bathymétrie ancienne au service de l’étude de tsunamis inexpliqués : le cas du pertuis d’Antioche (1785, 1875, 1882) / Helen Mair Rawsthorne in Norois, n° 263 (avril - juin 2022)PermalinkCoastal observation of sea surface tide and wave height using opportunity signal from Beidou GEO satellites: analysis and evaluation / Feng Wang in Journal of geodesy, vol 96 n° 4 (April 2022)PermalinkSimultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)PermalinkValidating a new GNSS-based sea level instrument (CalNaGeo) at Senetosa Cape / Pascal Bonnefond in Marine geodesy, vol 45 n° 2 (March 2022)PermalinkCo-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements / Mokhamad Nur Cahyadi in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkApport des nouveaux systèmes GNSS de cartographie du niveau marin à l’exploitation des données altimétriques en zone côtière / Clémence Chupin (2022)PermalinkCartographie dynamique de la topographie de l'océan de surface par assimilation de données altimétriques / Florian Le Guillou (2022)PermalinkContraintes observationnelles historiques sur la sensibilité climatique : implications pour les projections de la hausse du niveau de la mer / Jonathan Chenal (2022)PermalinkDetection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkModélisation du lien entre éruptions et glissements de flancs au Piton de la Fournaise / Quentin Dumont (2022)PermalinkShipborne GNSS acquisition of sea surface heights in the Baltic Sea / Aive Lilibusk in Journal of geodetic science, vol 12 n° 1 (January 2022)PermalinkGIS to identify exposed shoreline sectors to wave impacts: case of El Tarf coast / Abdeldjalil Goumrasa in Applied geomatics, vol 13 n° 4 (December 2021)PermalinkLa modélisation des eaux / Michel Kasser in Géomètre, n° 2197 (décembre 2021)PermalinkWhat is the impact of tectonic plate movement on country size? A long-term forecast / Kamil Maciuk in Remote sensing, vol 13 n° 23 (December-1 2021)PermalinkSpatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)PermalinkEfficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine / Yongjing Mao in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkEvaluation of global ocean tide models based on tidal gravity observations in China / Hongbo Tan in Geodesy and Geodynamics, vol 12 n° 6 (November 2021)PermalinkSeven decades of coastal change at Barter Island, Alaska: Exploring the importance of waves and temperature on erosion of coastal permafrost bluffs / Ann E. Gibbs in Remote sensing, vol 13 n° 21 (November-1 2021)PermalinkTidal flood area mapping in the face of climate change scenarios: case study in a tropical estuary in the Brazilian semi-arid region / Paulo Victor N. Araújo in Natural Hazards and Earth System Sciences, vol 21 n° 11 (November 2021)PermalinkOn the TEC bias of altimeter satellites / Francisco Azpilicueta in Journal of geodesy, vol 95 n° 10 (October 2021)PermalinkSeawater Debye model function at L-band and its impact on salinity retrieval from Aquarius satellite data / Yiwen Zhou in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkSentinel-6A precise orbit determination using a combined GPS/Galileo receiver / Oliver Montenbruck in Journal of geodesy, vol 95 n° 10 (October 2021)PermalinkProtection naturelle contre la submersion, apport de l'intelligence artificielle / Antoine Mury in Cartes & Géomatique, n° 245-246 (septembre - décembre 2021)PermalinkEstablishing vertical separation models for vulnerable coastlines in developing territories / Cassandra Nanlal in Marine geodesy, vol 44 n° 5 (September 2021)PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)PermalinkRemote sensing method for extracting topographic information on tidal flats using spatial distribution features / Yang Lijun in Marine geodesy, vol 44 n° 5 (September 2021)PermalinkComparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes / Shengjun Zhang in Journal of geodesy, vol 95 n° 6 (June 2021)PermalinkIdentifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing / Elliott White Jr in Remote sensing of environment, vol 258 (June 2021)PermalinkRetrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean / Kunpeng Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkLearning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)PermalinkMulticriterial method of AHP analysis for the identification of coastal vulnerability regarding the rise of sea level: case study in Ilha Grande Bay, Rio de Janeiro, Brazil / Julia Caon Araujo in Natural Hazards, vol 107 n° 1 (May 2021)PermalinkSNR-based water height retrieval in rivers: Application to high amplitude asymmetric tides in the Garonne river / Pierre Zeiger in Remote sensing, vol 13 n° 9 (May-1 2021)PermalinkValidating geoid models with marine GNSS measurements, sea surface models, and additional gravity observations in the Gulf of Finland / Timo Saari in Marine geodesy, vol 44 n° 3 (May 2021)PermalinkAtmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters / Quinten Vanhellemont in Remote sensing of environment, Vol 256 (April 2020)PermalinkTime-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine / Dong Liang in Remote sensing of environment, Vol 256 (April 2020)PermalinkAssessment of mass-induced sea level variability in the Tropical Indian Ocean based on GRACE and altimeter observations / Shiva Shankar Manche in Journal of geodesy, vol 95 n° 2 (February 2021)PermalinkCoastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkA GIS-based system for spatial-temporal availability evaluation of the open spaces used as emergency shelters: The case of Victoria, British Columbia, Canada / Yibing Yao in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)PermalinkMonitoring the coastal changes of the Po river delta (Northern Italy) since 1911 using archival cartography, multi-temporal aerial photogrammetry and LiDAR data: implications for coastline changes in 2100 A.D. / Massimo Fabris in Remote sensing, Vol 13 n° 3 (February 2021)PermalinkAccurate sea surface heights from Sentinel-3A and Jason-3 retrackers by incorporating high-resolution marine geoid and hydrodynamic models / Mir Abolfazl Mostafavi in Journal of geodetic science, vol 11 n° 1 (January 2021)PermalinkAssessment of chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data / Ioannis Moutzouris-Sidiris in Open geosciences, vol 13 n° 1 (January 2021)PermalinkComparing the performance of turbulent kinetic energy and K-profile parameterization vertical parameterization schemes over the tropical indian ocean / Lokesh Kumar Pandey in Marine geodesy, vol 44 n° 1 (January 2021)PermalinkDetermination of the lunar body tide from global laser altimetry data / Robin N. Thor in Journal of geodesy, vol 95 n° 1 (January 2021)PermalinkDynamics of inundation events in the rivers-estuaries-ocean continuum in Bengal delta : synergy between hydrodynamic modelling and spaceborne remote sensing / Md Jamal Uddin Kahn (2021)PermalinkLes impacts spatiaux du changement climatique / Denis Mercier (2021)PermalinkPermalinkPermalinkA method of hydrographic survey technology selection based on the decision tree supervised learning / Ivana Golub Medvešek (2021)PermalinkPermalinkRemotely-sensed rip current dynamics and morphological control in high-energy beach environments / Isaac Rodriguez Padilla (2021)PermalinkSea level acceleration under the magnifier / Huseyin Baki Iz in Journal of geodetic science, vol 11 n° 1 (January 2021)PermalinkSemantic segmentation of sea ice type on Sentinel-1 SAR data using convolutional neural networks / Alissa Kouraeva (2021)PermalinkStatistical analysis of vertical land motions and sea level measurements at the coast / Kevin Gobron (2021)PermalinkSub-daily polar motion from GPS, GLONASS, and Galileo / Radoslaw Zajdel in Journal of geodesy, vol 95 n° 1 (January 2021)PermalinkSuper-resolution of VIIRS-measured ocean color products using deep convolutional neural network / Xiaoming Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkThe ambiguous sea level rise at Brest’s 212 yearlong record elucidated / Huseyin Baki Iz in Journal of geodetic science, vol 11 n° 1 (January 2021)PermalinkThe Influence of camera calibration on nearshore bathymetry estimation from UAV Vvdeos / Gonzalo Simarro in Remote sensing, vol 13 n° 1 (January-1 2021)PermalinkThe influence of sea-level changes on geodetic datums along the east coast of China / Yang Liu in Marine geodesy, vol 44 n° 1 (January 2021)PermalinkDeep 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)PermalinkSelf-consistent determination of the Earth’s GM, geocenter motion and figure axis orientation / Alexandre Couhert in Journal of geodesy, vol 94 n° 12 (December 2020)PermalinkThe construction of sound speed field based on back propagation neural network in the global ocean / Junting Wang in Marine geodesy, vol 43 n° 6 (November 2020)PermalinkArctic tsunamis threaten coastal landscapes and communities – survey of Karrat Isfjord 2017 tsunami effects in Nuugaatsiaq, western Greenland / Mateusz C. Strzelecki in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)PermalinkShoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method / Audrey Minghelli in Remote sensing, vol 12 n° 16 (August-2 2020)PermalinkEstimates of spaceborne precipitation radar pulsewidth and beamwidth using sea surface echo data / Kaya Kanemaru in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)PermalinkBenefits of combining GPS and GLONASS for measuring ocean tide loading displacement / Majid Abbaszadeh in Journal of geodesy, vol 94 n° 7 (July 2020)PermalinkLong time-series remote sensing analysis of the periodic cycle evolution of the inlets and ebb-tidal delta of Xincun Lagoon, Hainan Island, China / Huaguo Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)PermalinkDORIS, 30 ans d'opérations continues au coeur de la performance des missions altimétriques pour l'océanographie et les applications géodésiques / Anonyme in XYZ, n° 163 (juin 2020)PermalinkValidation of Sentinel-3A SRAL coastal sea level data at high posting rate: 80 Hz / Ana Aldarias in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkImproved arctic ocean mass variability inferred from time-variable gravity with constraints and dual leakage correction / Dapeng Mu in Marine geodesy, Vol 43 n° 3 (May 2020)PermalinkOptimal lowest astronomical tide estimation using maximum likelihood estimator with multiple ocean models hybridization / Mohammed El-Diasty in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)PermalinkLe sol s'affaisse, l'eau monte [Delta du Gange-Brahmapoutre-Meghna] / Marielle Mayo in Géomètre, n° 2179 (avril 2020)PermalinkAssessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 / Anthony Campbell in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)PermalinkRecent sea level change in the black sea from satellite altimetry and tide gauge observations / Nevin Betül Avsar in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkUsing Ranked Probability Skill Score (RPSS) as Nonlocal Root-Mean-Square Errors (RMSEs) for Mitigating Wet Bias of Soil Moisture Ocean Salinity (SMOS) Soil Moisture / Ju Hyoung Lee in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 2 (February 2020)PermalinkPermalinkGlobal investigation of marine atmospheric boundary layer rolls using Sentinel-1 SAR data / Chen Wang (2020)PermalinkThe certitude of a global sea level acceleration during the satellite altimeter era / Huseyin Baki Iz in Journal of geodetic science, vol 10 n° 1 (January 2020)PermalinkPolarization dependence of azimuth cutoff from quad-pol SAR images / Huimin Li in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)PermalinkA global vertical datum defined by the conventional geoid potential and the Earth ellipsoid parameters / Hadi Amin in Journal of geodesy, vol 93 n°10 (October 2019)PermalinkIntroducing a vertical land motion model for improving estimates of sea level rates derived from tide gauge records affected by earthquakes / Anna Klos in GPS solutions, vol 23 n° 4 (October 2019)PermalinkSea level variation around Australia and its relation to climate indices / Armin Agha Karimi in Marine geodesy, vol 42 n° 5 (September 2019)PermalinkVertical land motion in the Southwest and Central Pacific from available GNSS solutions and implications for relative sea levels / Valérie Ballu in Geophysical journal international, vol 218 n° 3 (September 2019)PermalinkReal-time sea-level monitoring using Kalman filtering of GNSS-R data / Joakim Strandberg in GPS solutions, vol 23 n° 3 (July 2019)PermalinkSea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach / Jian Zhao in Marine geodesy, vol 42 n° 4 (July 2019)PermalinkAnalysis 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)PermalinkDeveloping a subswath-based wind speed retrieval model for sentinel-1 VH-Polarized SAR data over the ocean surface / Kangyu Zhang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkWave measurements with a modified HydroBall buoy using different GNSS processing strategies / Benoit Crépeau Gendron in Geomatica, vol 73 n° 1 (March 2019)Permalink