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Titre : Estuaries and coastal zones : dynamics and response to environmental changes Type de document : Monographie Auteurs : Jiayi Pan, Éditeur scientifique ; Adam Devlin, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 268 p. ISBN/ISSN/EAN : 978-1-78985-579-1 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Océanographie
[Termes IGN] altimétrie satellitaire par radar
[Termes IGN] correction atmosphérique
[Termes IGN] données Jason
[Termes IGN] données localisées
[Termes IGN] données ouvertes
[Termes IGN] eaux côtières
[Termes IGN] écosystème
[Termes IGN] ERS-1
[Termes IGN] estuaire
[Termes IGN] géodésie spatiale
[Termes IGN] Hong-Kong
[Termes IGN] image Envisat
[Termes IGN] marée océanique
[Termes IGN] modèle océanographique
[Termes IGN] océanographie spatiale
[Termes IGN] rayonnement électromagnétique
[Termes IGN] zone humideRésumé : (Editeur) Estuaries and their surrounding wetland regions are among the most productive ecosystems in the world, with more than half of humanity inhabiting their shores. Anthropogenic factors make estuaries highly susceptible to ecosystem degradation. Coastal waters are closely connected with human activity, and their dynamic processes may greatly affect coastal environments. This book provides a compendium of studies on estuarine dynamics, river plumes, and coastal water dynamics, studies that have investigated the changes in estuarine and coastal zones in response to sea-level rise and other environmental factors, and policy and management strategies to ensure the health and economy of coastal zones. This book aims to display novel frontiers in these fields and may help to inspire in-depth studies in the future. Note de contenu :
1. A 30-Year History of the Tides and Currents in Elkhorn Slough, California / William W. Broenkow and Laurence C. Breaker
2. Tidal Evolution Related to Changing Sea Level; Worldwide and Regional Surveys, and the Impact to Estuaries and Other Coastal Zones / Adam Thomas Devlin and Jiayi Pan
3. Linear and Nonlinear Responses to Northeasters Coupled with Sea Level Rise: A Tale of Two Bays / Stephen Moore, Huijie Xue, Neal R. Pettigrew and John Cannon
4. Coastal Altimetry: A Promising Technology for the Coastal Oceanography Community / Xi-Yu Xu, Ke Xu, Ying Xu and Ling-Wei Shi
5. Structure and Dynamics of Plumes Generated by Small Rivers / Alexander Osadchiev and Peter Zavialov
6. Circulations in the Pearl River Estuary: Observation and Modeling / Jiayi Pan, Wenfeng Lai and Adam Thomas Devlin
7. Phytoplankton Biomass and Environmental Descriptors of Water Quality of an Urban Lagoon / Marco V.J. Cutrim, Francinara S. Ferreira, Lisana F. Cavalcanti, Ana K.D.S. Sá, Andrea Christina Gomes de Azevedo-Cutrim and Ricardo Luvizotto Santos
8. Variations of the Absorption of Chromophoric Dissolved Organic Matter in the Pearl River Estuary / Xia Lei, Jiayi Pan and Adam Thomas Devlin
9. Response of Coastal Upwelling East of Hainan Island in the South China Sea to Sudden Impact and Long-Term Variability of Atmospheric Forcing / Lingling Xie and Mingming Li
10. Strengthening Democracy in Indonesian Marine Spatial Planning through Open Spatial Data / Adipandang Yudono and Permana Yudiarso
11. Environmental Problems and Coastal Mitigation in South America: Examples from Northeast Brazil and Northern Colombia / Vanda Claudino-Sales, Ping Wang, Fábio Perdigão Vasconcelos and Adely Pereira SilveiraNuméro de notice : 26785 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.77837 Date de publication en ligne : 25/03/2020 En ligne : https://doi.org/10.5772/intechopen.77837 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99910 INS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)
[article]
Titre : INS/GNSS integration using recurrent fuzzy wavelet neural networks Type de document : Article/Communication Auteurs : Parisa Doostdar, Auteur ; Jafar Keighobadi, Auteur ; Mohammad Ali Hamed, Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification floue
[Termes IGN] couplage GNSS-INS
[Termes IGN] données GNSS
[Termes IGN] filtre de Kalman
[Termes IGN] interruption du signal
[Termes IGN] ondelette
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau neuronal récurrent
[Termes IGN] vitesse
[Vedettes matières IGN] Traitement de données GNSSRésumé : (Auteur) In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages. Numéro de notice : A2020-019 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0942-z Date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1007/s10291-019-0942-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94458
in GPS solutions > vol 24 n° 1 (January 2020)[article]Kalman filtering with state constraints applied to multi-sensor systems and georeferencing / Sören Vogel (2020)
Titre : Kalman filtering with state constraints applied to multi-sensor systems and georeferencing Type de document : Thèse/HDR Auteurs : Sören Vogel, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 856 Importance : 144 p. ISBN/ISSN/EAN : 978-3-7696-5268-0 Note générale : bibliographie
Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Universität Hannover ISSN 0174-1454, Nr. 364, 2020Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] contrainte d'intégrité
[Termes IGN] convolution (signal)
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] géoréférencement direct
[Termes IGN] positionnement cinématique
[Termes IGN] programmation par contraintes
[Termes IGN] semis de pointsRésumé : (auteur) Active research on the development of autonomous vehicles has been carried out for several years now. However, some significant challenges still need to be solved in this context. Particularly relevant is the constant guarantee and assurance of the integrity of such autonomous systems. In order to ensure safe manoeuvring in the direct environment of humans, an accurate, precise, reliable and continuous determination of the vehicle’s position and orientation is mandatory. In geodesy, this process is also referred to as georeferencing with respect to a superordinate earth-fixed coordinate system. Especially for complex inner-city areas, there are no fully reliable methods available so far. The otherwise suitable and therefore common Global Navigation Satellite System (GNSS) observations can fail in urban canyons. However, this fact does not only apply exclusively to autonomous vehicles but can generally also be transferred to any kinematic Multi-Sensor System (MSS) operating within challenging environments. Especially in geodesy, there are many MSSs, which require accurate and reliable georeferencing regardless of the environment. This is indispensable for derived subsequent products, such as highly accurate three-dimensional point clouds for 3D city models or Building Information Modelling (BIM) applications. The demand for new georeferencing methods under aspects of integrity also involves the applicability of big data. Modern sensors for capturing the environment, e.g. laser scanners or cameras, are becoming increasingly cheaper and also offer higher information density and accuracy. For many kinematic MSSs, this change leads to a steady increase in the amount of acquired observation data. Many of the currently methods used are not suitable for processing such amounts of data, and instead, they only use a random subset. Besides, big data also influences potential requirements with regard to possible real-time applications. If there is no excessive computing power available to take into account the vast amounts of observation data, recursive methods are usually recommended. In this case, an iterative estimation of the requested quantities is performed, whereby the comprehensive total data set is divided into several individual epochs. If the most recent observations are successively available for each epoch, a filtering algorithm can be applied. Thus, an efficient estimation is carried out and, with respect to a comprehensive overall adjustment, generally larger observation sets can be considered. However, such filtering algorithms exist so far almost exclusively for explicit relations between the available observations and the requested estimation quantities.
If this mathematical relationship is implicit, which is certainly the case for several practical issues, only a few methods exist or, in the case of recursive parameter estimation, none at all. This circumstance is accompanied by the fact that the combination of implicit relationships with constraints regarding the parameters to be estimated has not yet been investigated at all. In this thesis, a versatile filter algorithm is presented, which is valid for explicit and for implicit mathematical relations as well. For the first time, methods for the consideration of constraints are given, especially for implicit relations. The developed methodology will be comprehensively validated and evaluated by simulations and real-world application examples of practical relevance. The usage of real data is directly related to kinematic MSSs and the related tasks of calibration and georeferencing. The latter especially with regard to complex innercity environments. In such challenging environments, the requirements for georeferencing under integrity aspects are of special importance. Therefore, the simultaneous use of independent and complementary information sources is applied in this thesis. This enables a reliable georeferencing solution to be achieved and a prompt notification to be issued in case of integrity violations.Note de contenu : 1- Introduction
2- Fundamentals of Recursive State-space Filtering
3- Methodological contributions
4- Kinematic Multi-sensor Systems and Their Efficient Calibration
5- Information-based Georeferencing
6- ConclusionsNuméro de notice : 17686 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geodäsie und Geoinformatik : Hanovre : 2020 DOI : sans En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-856.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98164 Learning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)
Titre : Learning and geometric approaches for automatic extraction of objects from remote sensing images Type de document : Thèse/HDR Auteurs : Nicolas Girard, Auteur Editeur : Nice : Université Côte d'Azur Année de publication : 2020 Importance : 169 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat Présentée en vue de l’obtention du grade de docteur en Automatique, Traitement du Signal et des Images de l'Université Côte d’AzurLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] alignement
[Termes IGN] appariement de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] erreur
[Termes IGN] figure géométrique
[Termes IGN] filtrage du bruit
[Termes IGN] jeu de données
[Termes IGN] polygonation
[Termes IGN] réalité de terrain
[Termes IGN] segmentation d'image
[Termes IGN] vectorisationIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Creating a digital double of the Earth in the form of a map has many applications in e.g. autonomous driving, automated drone delivery, urban planning, telecommunications, and disaster management. Geographic Information Systems (GIS) are the frameworks used to integrate geolocalized data and represent maps. They represent shapes of objects in a vector representation so that it is as sparse as possible while representing shapes accurately, as well as making it easier to edit than raster data. With the increasing amount of satellite and aerial images being captured every day, automatic methods are being developed to transfer the information found in those remote sensing images into Geographic Information Systems. Deep learning methods for image segmentation are able to delineate the shapes of objects found in images, but they do so with a raster representation, in the form of a mask. Post-processing vectorization methods then convert that raster representation into a vector representation compatible with GIS. Another challenge in remote sensing is to deal with a certain type of noise in the data, which is the misalignment between different layers of geolocalized information (e.g. between images and building cadaster data). This type of noise is frequent due to various errors introduced during the processing of remote sensing data. This thesis develops combined learning and geometric approaches with the purpose to improve automatic GIS mapping from remote sensing images. We first propose a method for correcting misaligned maps over images, with the first motivation for them to match, but also with the motivation to create remote sensing datasets for image segmentation with alignment-corrected ground truth. Indeed training a model on misaligned ground truth would not lead to a nice segmentation, whereas aligned ground truth annotations will result in better segmentation models. During this work we also observed a denoising effect of our alignment model and use it to denoise a misaligned dataset in a self-supervised manner, meaning only the misaligned dataset was used for training.
We then propose a simple approach to use a neural network to directly output shape information in the vector representation, in order to by-pass the post-processing vectorization step. Experimental results on a dataset of solar panels show that the proposed network succeeds in learning to regress polygon coordinates, yielding directly vectorial map outputs. Our simple method is limited to predicting polygons with a fixed number of vertices though. While more recent methods for learning directly in the vector representation are not limited to a fixed number of vertices, they still have other limitations in terms of the type of object shapes they can predict. More complex topological cases such as objects with holes or buildings touching each other (with a common wall which is very typical of European city centers) are not handled by these fully deep learning methods. We thus propose a hybrid approach alleviating those limitations by training a neural network to output a segmentation probability map as usual and also to output a frame field aligned with the contours of detected objects (buildings in our case). The frame field constitutes additional shape information learned by the network. We then propose our highly parallelizable polygonization method for leveraging that frame field information to vectorize the segmentation probability map efficiently. Because our polygonization method has access to additional information in the form of a frame field, it can be less complex than other advanced vectorization methods and is thus faster. Lastly, requiring an image segmentation network to also output a frame field only adds two convolutional layers and virtually does not increase inference time, making the use of a frame field only beneficial.Note de contenu : 1- Introduction
2- Building alignment
3- Building alignment from noisy ground truth
4- PolyCNN: learning polygons
5- Frame field learning
6- Polygonization by frame field
7- Conclusions and perspectivesNuméro de notice : 28501 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du Signal et des Images : Côte d’Azur : 2020 Organisme de stage : Inria Sophia-Antipolis nature-HAL : Thèse DOI : sans En ligne : https://hal.inria.fr/tel-03111628/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96940 Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network / Yuxing Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)
[article]
Titre : Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network Type de document : Article/Communication Auteurs : Yuxing Zhao, Auteur ; Yue Li, Auteur ; Baojun Yang, Auteur Année de publication : 2020 Article en page(s) : pp 650 - 665 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] algorithme de filtrage
[Termes IGN] analyse multiéchelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] désert
[Termes IGN] enregistrement de données
[Termes IGN] filtrage du bruit
[Termes IGN] filtre passe-bande
[Termes IGN] interruption du signal
[Termes IGN] lutte contre le bruit
[Termes IGN] rapport signal sur bruit
[Termes IGN] reconstruction du signal
[Termes IGN] séismeRésumé : (auteur) Existing denoising algorithms often need to meet some premise assumptions and applicable conditions, such as the signal-to-noise ratio (SNR) cannot be too low, and the noise needs to obey a specific distribution (such as Gaussian distribution) and to satisfy some properties (such as stationarity). For the desert noise that shares the same frequency band with the effective signal and has complex characteristics (nonlinear, nonstationary, and non-Gaussian), it is difficult to find a universally applicable method. In response to this problem, a multiscale geometric analysis (MGA) convolutional neural network (CNN) is proposed in this article. One of the most important features of the CNN is that it can extract data-rich intrinsic information from the training set without relying on a priori assumption. By introducing the CNN into the MGA, a new kind of denoising method can be created, which can achieve good results even under a low SNR. This article takes the nonsubsampled contourlet transform as an example to create a denoising network named NC-CNN for high-efficiency and intelligent denoising of desert seismic data. The processing results of synthetic seismic records and field seismic records prove that NC-CNN can effectively suppress the low-frequency noise (random noise and surface wave), and the effective signal almost has no energy loss. In addition, the reconstruction ability of the missing signals is also an advantage of this method. Numéro de notice : A2020-076 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2938836 Date de publication en ligne : 24/09/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2938836 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94608
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 650 - 665[article]Manuel d'optique / Germain Chartier (2020)PermalinkPermalinkPermalinkSimulation d’éclairements des surfaces ombrées en zone urbaine par transfert radiatif 3D (modèle DART) / Yulu Xi (2020)PermalinkSmoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors / Paul Chauchat (2020)PermalinkContextual filtering methods based on the subbands and subspaces decomposition of complex SAR interferograms / Saoussen Belhadj-Aissa in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 12 n° 12 (December 2019)PermalinkModelling of the timeseries of GNSS coordinates and their interaction with average magnitude earthquakes / Sanja Tucikesic in Geodetski vestnik, Vol 63 n° 4 (December 2019)PermalinkIntroducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkRobust acquisition at GPS receivers in unsafe locations using complex wavelet transform / M. Moazedi in Survey review, vol 51 n° 369 (November 2019)PermalinkExperimental results of multipath behavior for GPS L1-L2 and Galileo E1-E5b in static and kinematic scenarios / Alexandra Avram in Journal of applied geodesy, Vol 13 n° 4 (October 2019)PermalinkKalman-filter-based undifferenced cycle slip estimation in real-time precise point positioning / Pan Li in GPS solutions, vol 23 n° 4 (October 2019)PermalinkMeasuring phase scintillation at different frequencies with conventional GNSS receivers operating at 1 Hz / Viet Khoi Nguyen in Journal of geodesy, vol 93 n°10 (October 2019)PermalinkSaliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)PermalinkAn analytic expression for the phase noise of the goldstein–werner filter / Scott Hensley in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkAnalysis of higher-order ionospheric effects on GNSS precise point positioning in the China area / Yaozong Zhou in Survey review, vol 51 n° 368 (September 2019)PermalinkCombination of GRACE monthly gravity fields on the normal equation level / Ulrich Meyer in Journal of geodesy, vol 93 n° 9 (September 2019)PermalinkComparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey / Baris Suleymanoglu in Geodetski vestnik, vol 63 n° 3 (September - November 2019)PermalinkDecomposition of geodetic time series: A combined simulated annealing algorithm and Kalman filter approach / Feng Ming in Advances in space research, vol 64 n°5 (1 September 2019)PermalinkEvaluating the impact of higher-order ionospheric corrections on multi-GNSS ultra-rapid orbit determination / Xinghan Chen in Journal of geodesy, vol 93 n° 9 (September 2019)PermalinkA filtering-based approach for improving crowdsourced GNSS traces in a data update context / Stefan Ivanovic in ISPRS International journal of geo-information, vol 8 n° 9 (September 2019)Permalink