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Artificial intelligence oceanography / Xiaofeng Li (2023)  

Public 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 mer Résumé : (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 learning Numéro 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

Sea surface roughness observed by high resolution radar / Atsushi Fujimura (2019)  

Public Titre : Sea surface roughness observed by high resolution radar Type de document : Monographie Auteurs : Atsushi Fujimura, Éditeur scientifique ; Susanne Lehner, Éditeur scientifique ; Alex Soloviev, Éditeur scientifique ; Xiaofeng Li, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 202 p. ISBN/ISSN/EAN : 978-3-03921-747-2 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande X [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] rugosité [Termes IGN] surface de la mer [Termes IGN] télédétection en hyperfréquence [Termes IGN] télédétection spatiale [Termes IGN] turbulence [Termes IGN] vague [Termes IGN] vent Résumé : (Auteur) Changes in sea surface roughness are usually associated with a change in the sea surface wind field. This interaction has been exploited to measure sea surface wind speed by scatterometry. A number of features on the sea surface associated with changes in roughness can be observed by synthetic aperture radar (SAR) because of the change in Bragg backscatter of the radar signal by damping of the resonant ocean capillary waves. With various radar frequencies, resolutions, and modes of polarization, sea surface features have been analyzed in numerous campaigns, bringing various datasets together, thus allowing for new insights into small-scale processes at a larger areal coverage. This Special Issue aims at investigating sea surface features detected by high spatial resolution radar systems, such as SAR. Numéro de notice : 26504 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03921-747-2 En ligne : https://doi.org/10.3390/books978-3-03921-747-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97078

On spectral unmixing resolution using extended support vector machines / Xiaofeng Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 4985 - 4996 Titre : On spectral unmixing resolution using extended support vector machines Type de document : Article/Communication Auteurs : Xiaofeng Li, Auteur ; Xiuping Jia, Auteur ; Liguo Wang, Auteur ; Kai Zhao, Auteur Année de publication : 2015 Article en page(s) : pp 4985 - 4996 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse infrapixellaire [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification pixellaire [Termes IGN] classification spectrale Résumé : (Auteur) Due to the limited spatial resolution of multispectral/hyperspectral data, mixed pixels widely exist and various spectral unmixing techniques have been developed for information extraction at the subpixel level in recent years. One of the challenging problems in spectral mixture analysis is how to model the data of a primary class. Given that the within-class spectral variability (WSV) is inevitable, it is more realistic to associate a group of representative spectra with a pure class. The unmixing method using the extended support vector machines (eSVMs) has handled this problem effectively. However, it has simplified WSV in the mixed cases. In this paper, a further development of eSVMs is presented to address two problems in multiple-endmember spectral mixture analysis: 1) one mixed pixel may be unmixed into different fractions (model overlap); and 2) one fraction may correspond to a group of mixed pixels (fraction overlap). Then, spectral unmixing resolution (SUR) is introduced to characterize how finely the mixture in a mixed pixel can be quantified. The quantitative relationship between SUR and WSV of endmembers is derived via a geometry analysis in support vector machine feature space. Thus, the possible SUR can be estimated when multiple endmembers for each class are given. Moreover, if the requirement of SUR is fixed, the acceptance level of WSV is then limited, which can be used as a guide to remove outliers and purify endmembers for each primary class. Experiments are presented to illustrate model and fraction overlap problems and the application of SUR in uncertainty analysis of spectral unmixing. Numéro de notice : A2015-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2415587 Date de publication en ligne : 21/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2415587 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77555 [article]

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