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Assessing a new velocity field in Greece towards a new semi-kinematic datum / S. Bitharis in Survey review, vol 51 n° 368 (September 2019)
[article]
Titre : Assessing a new velocity field in Greece towards a new semi-kinematic datum Type de document : Article/Communication Auteurs : S. Bitharis, Auteur ; Nestoras Papadopoulos, Auteur ; C. Pikridas, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 450 - 459 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] champ de vitesse
[Termes IGN] déformation de la croute terrestre
[Termes IGN] géodynamique
[Termes IGN] Grèce
[Termes IGN] réseau géodésique local
[Termes IGN] système de référence localRésumé : (Auteur) Greece has always been an interesting study area for the geodesists due to its intense geodynamic behaviour. Especially, the exploitation of a modern geodetic velocity field using GNSS becomes a crucial requirement for geodetic purposes. The main aim of the present study is the assessment of a modern geodetic velocity model. This procedure is based on the unification of various reference frames, implemented from individual extended GNSS campaigns in different epochs with the effective involvement of the Hellenic Military Geographic Service. The implementation of geodetic velocities in the inhomogeneous Greek velocity field improves significantly the reliability of transformations, more than 60% with respect to the horizontal component, applying a vice versa time shift to a common epoch. We also emphasise the necessity to adopt a velocity model in order to process regional GNSS networks and therefore to establish a modern semi-kinematic reference frame in Greece. Numéro de notice : A2019-386 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1479937 Date de publication en ligne : 02/06/2018 En ligne : https://doi.org/10.1080/00396265.2018.1479937 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93476
in Survey review > vol 51 n° 368 (September 2019) . - pp 450 - 459[article]Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)
[article]
Titre : Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors Type de document : Article/Communication Auteurs : Pauline Perbet, Auteur ; Michelle Fortin, Auteur ; Anouk Ville, Auteur ; Martin Béland, Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 7439 - 7458 Note générale : bibliographie
This work was supported by the Natural Sciences and Engineering Research Council of Canada.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse vectorielle
[Termes IGN] déboisement
[Termes IGN] défrichement
[Termes IGN] détection de changement
[Termes IGN] forêt tropicale
[Termes IGN] image captée par drone
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Indonésie
[Termes IGN] Malaisie
[Termes IGN] surveillance forestièreRésumé : (auteur) Malaysia and Indonesia have been affected by deforestation caused in great part by the proliferation of oil palm plantations. To survey this loss of forest, several studies have monitored these southeast Asian nations with satellite remote sensing alert systems. The methods used have shown potential for this approach, but they are limited by imagery with coarse spatial resolution, low revisit times, and cloud cover. The objective of this research is to improve near real-time operational deforestation detection by combining three sensors: Sentinel-1, Sentinel-2 and Landsat-8. We used Change Vector Analysis to detect changes between non-affected forest and images under analysis. The results were validated using 166 plots of undisturbed forest and confirmed deforestation events throughout Sabah Malaysian State, and from 70 points from drone pictures in Sumatra, Indonesia. Sentinel-2 and Landsat-8 yielded sufficient results in terms of accuracy (less than 11% of commission and omission error). Sentinel-1 had lower accuracy (14% of commission error and 28% of omission error), probably resulting from geometric distortions and speckle noise. During the high cloud-cover season optical sensors took about twice the time to detect deforestation compared to Sentinel-1 which was not affected by cloud cover. By combining the three sensors, we detected deforestations about 8 days after forest clearing events. Deforestations were only detectable during approximately the first 100 days, before bare soils were often coved by legume crop. Our results indicate that near real-time deforestation detection can reveal most events, but the number of false detections could be improved using a multiple event detection process. Numéro de notice : A2019-321 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1579390 Date de publication en ligne : 17/02/2019 En ligne : https://doi.org/10.1080/01431161.2019.1579390 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93295
in International Journal of Remote Sensing IJRS > vol 40 n°19 (February 2019) . - pp 7439 - 7458[article]
Titre : Foundations of deep convolutional models through kernel methods Type de document : Thèse/HDR Auteurs : Alberto Bietti, Auteur ; Julien Mairal, Directeur de thèse Editeur : Grenoble : Université de Grenoble Année de publication : 2019 Importance : 194 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour obtenir le grade de Docteur de la Communauté Université Grenoble Alpes, Spécialité : Mathématiques AppliquéesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] approche hiérarchique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espace de Hilbert
[Termes IGN] état de l'art
[Termes IGN] invariance
[Termes IGN] jeu de données
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] optimisation (mathématiques)
[Termes IGN] Perceptron multicoucheIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The increased availability of large amounts of data, from images in social networks, speech waveforms from mobile devices, and large text corpuses, to genomic and medical data, has led to a surge of machine learning techniques. Such methods exploit statistical patterns in these large datasets for making accurate predictions on new data. In recent years, deep learning systems have emerged as a remarkably successful class of machine learning algorithms, which rely on gradient-based methods for training multi-layer models that process data in a hierarchical manner. These methods have been particularly successful in tasks where the data consists of natural signals such as images or audio; this includes visual recognition, object detection or segmentation, and speech recognition.For such tasks, deep learning methods often yield the best known empirical performance; yet, the high dimensionality of the data and large number of parameters of these models make them challenging to understand theoretically. Their success is often attributed in part to their ability to exploit useful structure in natural signals, such as local stationarity or invariance, for instance through choices of network architectures with convolution and pooling operations. However, such properties are still poorly understood from a theoretical standpoint, leading to a growing gap between the theory and practice of machine learning. This thesis is aimed towards bridging this gap, by studying spaces of functions which arise from given network architectures, with a focus on the convolutional case. Our study relies on kernel methods, by considering reproducing kernel Hilbert spaces (RKHSs) associated to certain kernels that are constructed hierarchically based on a given architecture. This allows us to precisely study smoothness, invariance, stability to deformations, and approximation properties of functions in the RKHS. These representation properties are also linked with optimization questions when training deep networks with gradient methods in some over-parameterized regimes where such kernels arise. They also suggest new practical regularization strategies for obtaining better generalization performance on small datasets, and state-of-the-art performance for adversarial robustness on image tasks. Note de contenu : 1- Introduction
2- Invariance, Stability to deformations, and complexity of deep convolutional representations
3- A kernel perspective on regularization and robustness of deep neural networks
4- Links with optimization: inductive bias of neural tangent kernels
5- Invariance and stability through regularization: a stochastic optimization algorithm for data augmentation
6- Conclusion and perspectivesNuméro de notice : 25833 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques Appliquées : Grenoble Alpes : 2019 nature-HAL : Thèse DOI : sans En ligne : https://hal.archives-ouvertes.fr/tel-02543073/ document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95171 Computing with cognitive spatial frames of reference in GIS / Simon Scheider in Transactions in GIS, vol 22 n° 5 (October 2018)
[article]
Titre : Computing with cognitive spatial frames of reference in GIS Type de document : Article/Communication Auteurs : Simon Scheider, Auteur ; Jürgen Hahn, Auteur ; Paul Weiser, Auteur ; Werner Kuhn, Auteur Année de publication : 2018 Article en page(s) : pp 1083 - 1104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] espace vectoriel
[Termes IGN] logique floue
[Termes IGN] modèle cognitif
[Termes IGN] transformation géométriqueRésumé : (Auteur) In everyday communication, people effortlessly translate between spatial cognitive frames of reference. For example, a tourist guide translates from a map (“the fountain is north‐west of the church”) into a cognitive frame for a tourist (“the fountain in front of the church”). While different types of cognitive reference frames and their relevance for language cultures have been studied in considerable depth, we still lack adequate transformation models. In this article, we argue that transformations in current Geographic Information Systems (GIS) are inappropriate to this end. Appropriate transformation models need to go beyond point discretization to take into account vague transformations, in order to deal with forms, sizes, and vagueness of spatial relations relative to ground objects. We argue that neural fields should be used to denote fuzzy positions, directions, and sizes in a particular frame. We propose fuzzy vector spaces to approximate neural field behavior with affine transformations, including fuzzy translation, rotation, and scaling, in order to efficiently transform between different cognitive perspectives. We use an implementation in Haskell to describe a geographic map from the perspective of six well‐known cognitive frames of reference. Based on these findings, we give an outlook on the principles of a “neural GIS.” Numéro de notice : A2018-570 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12318 Date de publication en ligne : 11/10/2018 En ligne : https://doi.org/10.1111/tgis.12318 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92289
in Transactions in GIS > vol 22 n° 5 (October 2018) . - pp 1083 - 1104[article]Influences of environmental loading corrections on the nonlinear variations and velocity uncertainties for the reprocessed global positioning system height time series of the crustal movement observation network of China / Peng Yuan in Remote sensing, vol 10 n° 6 (June 2018)
[article]
Titre : Influences of environmental loading corrections on the nonlinear variations and velocity uncertainties for the reprocessed global positioning system height time series of the crustal movement observation network of China Type de document : Article/Communication Auteurs : Peng Yuan, Auteur ; Zhao Li, Auteur ; Weiping Jiang, Auteur ; Yifang Ma , Auteur ; Wu Chen, Auteur ; Nico Sneeuw, Auteur Année de publication : 2018 Projets : 1-Pas de projet / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] analyse en composantes principales
[Termes IGN] champ de vitesse
[Termes IGN] Chine
[Termes IGN] coordonnées GPS
[Termes IGN] correction géométrique
[Termes IGN] données GPS
[Termes IGN] réseau de surveillance géophysique
[Termes IGN] série temporelle
[Termes IGN] station permanenteRésumé : (auteur) Mass redistribution of the atmosphere, oceans, and terrestrial water storage generates crustal displacements which can be predicted by environmental loading models and observed by the Global Positioning System (GPS). In this paper, daily height time series of 235 GPS stations derived from a homogeneously reprocessed Crustal Movement Observation Network of China (CMONOC) and corresponding loading displacements predicted by the Deutsche GeoForschungsZentrum (GFZ) are compared to assess the effects of loading corrections on the nonlinear variations of GPS time series. Results show that the average root mean square (RMS) of vertical displacements due to atmospheric, nontidal oceanic, hydrological, and their combined effects are 3.2, 0.6, 2.7, and 4.0 mm, respectively. Vertical annual signals of loading and GPS are consistent in amplitude but different in phase systematically. The average correlation coefficient between loading and GPS height time series is 0.6. RMS of the GPS height time series are reduced by 20% on average. Moreover, an investigation of 208 CMONOC stations with observing time spans of ~4.6 years shows that environmental loading corrections lead to an overestimation of the GPS velocity uncertainty by about 1.4 times on average. Nevertheless, by using a common mode component filter through principal component analysis, the dilution of velocity precision due to environmental loading corrections can be compensated. Numéro de notice : A2018-658 Affiliation des auteurs : LASTIG LAREG+Ext (2012-mi2018) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs10060958 Date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.3390/rs10060958 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93811
in Remote sensing > vol 10 n° 6 (June 2018)[article]Documents numériques
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