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Titre : Spatial machine learning applied to multivariate and multimodal images Type de document : Thèse/HDR Auteurs : Gianni Franchi, Auteur ; Jesus Angulo lopez, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2016 Importance : 197 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université de Recherche Paris Sciences et Lettres, préparée à MINES ParisTech, Spécialité : Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] krigeage
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] microscope électronique
[Termes IGN] morphologie mathématique
[Termes IGN] régression linéaireIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis focuses on multivariate spatial statistics and machine learning applied to hyperspectral and multimodal and images in remote sensing and scanning electron
microscopy (SEM). In this thesis the following topics are considered:
Fusion of images: SEM allows us to acquire images from a given sample using different modalities. The purpose of these studies is to analyze the interest of fusion of information to improve the multimodal SEM images acquisition. We have modeled
and implemented various techniques of image fusion of information, based in
particular on spatial regression theory. They have been assessed on various
datasets.
Spatial classification of multivariate image pixels: We have proposed a novel approach for pixel classification in multi/hyperspectral images. The aim of this technique is to represent and efficiently describe the spatial/spectral features of multivariate images. These multi-scale deep descriptors aim at representing the content of the image while considering invariances related to the texture and to its geometric transformations.
Spatial dimensionality reduction: We have developed a technique to extract a feature space using morphological principal component analysis. Indeed, in order to take into account the spatial and structural information we used mathematical morphology operatorsNote de contenu : I- Introduction
II- Feature representation and classification for hyperspectral images
III- Fusion of information for multimodal SEM images
IV ConclusionNuméro de notice : 25828 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Morphologie Mathématique : Paris, 2016 nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-01483980v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95124
Titre : Statistical learning from a regression perspective Type de document : Guide/Manuel Auteurs : Richard A. Berk, Auteur Editeur : Springer International Publishing Année de publication : 2016 ISBN/ISSN/EAN : 978-3-319-44048-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de données
[Termes IGN] arbre aléatoire
[Termes IGN] classification et arbre de régression
[Termes IGN] ensachage
[Termes IGN] régression
[Termes IGN] régression multivariée par spline adaptative
[Termes IGN] régression par quantile
[Termes IGN] séparateur à vaste margeRésumé : (éditeur) This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. A principal instance is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Also provided is helpful craft lore such as not automatically ceding data analysis decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important message is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R with code routinely provided. Note de contenu : 1- Statistical Learning as a Regression Problem
2- Splines, Smoothers, and Kernels
3- Classification and Regression Trees (CART)
4- Bagging
5- Random Forests
6- Boosting
7- Support Vector Machines
8- Some Other Procedures Briefly
9- Broader Implications and a Bit of Craft LoreNuméro de notice : 25800 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel de cours DOI : 10.1007/978-3-319-44048-4 En ligne : https://doi.org/10.1007/978-3-319-44048-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95043
Titre : Stochastic dynamic programming Type de document : Guide/Manuel Auteurs : Kjetil Kåre Haugen, Auteur Editeur : Oslo [Norvège] : Universitetsforlaget - Scandinavian University Press Année de publication : 2016 Importance : 105 p. ISBN/ISSN/EAN : 978-82-15-02671-8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Mathématique
[Termes IGN] arbre de décision
[Termes IGN] méthode déterministe
[Termes IGN] optimisation (mathématiques)
[Termes IGN] processus stochastique
[Termes IGN] programmation dynamique
[Termes IGN] programmation stochastique
[Termes IGN] variable aléatoireRésumé : (éditeur) This text gives a comprehensive coverage of how optimization problems involving decisions and uncertainty may be handled by the methodology of Stochastic Dynamic Programming (SDP). A rapidly changing world with seemingly growing uncertainty needs a modern approach to this classic methodology. The book treats discrete, as well as continuous problems, all illustrated by relevant real world examples. The book presents a comprehensive outline of SDP from its roots during World War II until today. Much of recent research are covered, as well as parts of the authors’ own original research. Algorithms and computer techniques are added when needed. The book may serve as a supplementary text book on SDP (preferably at the graduate level) given adequate added background material. Note de contenu : 1- Introduction
2- SDP – basic concepts
3- SDP - Benefits
4- SDP - difficulties
5- Infinite horizon problems
6- Recent researchNuméro de notice : 25972 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Manuel de cours DOI : 10.18261/9788215026718-2016 En ligne : https://doi.org/10.18261/9788215026718-2016 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96619 Towards process validation for complex transport models: A sensitivity analysis of a social network-enhanced activity-travel model / Nicole Ronald in Computers, Environment and Urban Systems, vol 55 (January 2016)
[article]
Titre : Towards process validation for complex transport models: A sensitivity analysis of a social network-enhanced activity-travel model Type de document : Article/Communication Auteurs : Nicole Ronald, Auteur ; Theo Arentze, Auteur ; Harry Timmermans, Auteur Année de publication : 2016 Article en page(s) : pp 24 - 32 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] agent (intelligence artificielle)
[Termes IGN] analyse de sensibilité
[Termes IGN] comportement
[Termes IGN] modèle orienté agent
[Termes IGN] réseau social
[Termes IGN] transport urbainRésumé : (auteur) Model validation is a significant issue for the modelling of social network-based transportation models because of the many interacting components (the individuals, the environment, and now the network) in the model.
In this paper we focus on a sensitivity analysis for such a model, which is part of a larger validation approach known as process validation. This approach investigates both the structure and behaviour of the model, to evaluate whether the model can be used for prediction.
The paper draws on a novel set of experiments with an agent-based model which was developed to explore the effects of social networks on activity and travel behaviour. Several versions of the model were created, beginning with a single day model with no interaction, and then adding in multi-day runs with interactions, in order to demonstrate the validation process.
The paper argues that testing the model at different levels of complexity increases confidence in the model and makes it easier to locate components or functionality that require improvement. It concludes by suggesting that this approach to sensitivity testing should be adopted for validation of complex transportation models.Numéro de notice : A2016-391 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2015.09.005 Date de publication en ligne : 22/10/2015 En ligne : http://dx.doi.org/10.1016/j.compenvurbsys.2015.09.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81205
in Computers, Environment and Urban Systems > vol 55 (January 2016) . - pp 24 - 32[article]An exploration of future patterns of the contributions to OpenStreetMap and development of a contribution index / Jamal Jokar Arsanjani in Transactions in GIS, vol 19 n° 6 (December 2015)
[article]
Titre : An exploration of future patterns of the contributions to OpenStreetMap and development of a contribution index Type de document : Article/Communication Auteurs : Jamal Jokar Arsanjani, Auteur ; Peter Mooney, Auteur ; Marco Helbich, Auteur ; Alexander Zipf, Auteur Année de publication : 2015 Article en page(s) : pp 896 – 914 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] Allemagne
[Termes IGN] approche participative
[Termes IGN] automate cellulaire
[Termes IGN] base de données spatiotemporelles
[Termes IGN] cartographie collaborative
[Termes IGN] données localisées des bénévoles
[Termes IGN] indexation sémantique
[Termes IGN] OpenStreetMap
[Termes IGN] StuttgartRésumé : (auteur) OpenStreetMap (OSM) represents one of the most well-known examples of a collaborative mapping project. Major research efforts have so far dealt with data quality analysis but the modality of OSM's evolution across space and time has barely been noted. This study aims to analyze spatio-temporal patterns of contributions in OSM by proposing a contribution index (CI) in order to investigate the dynamism of OSM. The CI is based on a per cell analysis of the node quantity, interactivity, semantics, and attractivity (the ability to attract contributors). Additionally this research explores whether OSM has been constantly attracting new users and contributions or if OSM has experienced a decline in its ability to attract continued contributions. Using the Stuttgart region of Germany as a case study the empirical findings of the CI over time confirm that since 2007, OSM has been constantly attracting new users, who create new features, edit the existing spatial objects, and enrich them with attributes. This rate has been dramatically growing since 2011. The utilization of a Cellular Automata-Markov (CA-Markov) model provides evidence that by the end of 2016 and 2020, the rise of CI will spread out over the study area and only a few cells without OSM features will remain. Numéro de notice : A2016-437 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12139 En ligne : http://dx.doi.org/10.1111/tgis.12139 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81347
in Transactions in GIS > vol 19 n° 6 (December 2015) . - pp 896 – 914[article]A back-propagation neural network-based approach for multi-represented feature matching in update propagation / Yanxia Wang in Transactions in GIS, vol 19 n° 6 (December 2015)PermalinkBase de connaissances pour gérer des styles de rendus 3D / Mickaël Brasebin in Cartes & Géomatique, n° 226 (décembre 2015)PermalinkOptimal spatial land-use allocation for limited development ecological zones based on the geographic information system and a genetic ant colony algorithm / Nan Mi in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)PermalinkRoad vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation / Fateme Ameri in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)PermalinkA semi-automatic lightweight ontology bridging for the semantic integration of cross-domain geospatial information / Jung-Hong Hong in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)PermalinkLes services Istex : Istex au-delà de l'acquisition / Jean-Marie Pierrel in Arabesques, n° 80 (octobre - décembre 2015)PermalinkWide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing / Jerome O’Connell in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)PermalinkCarte de Kohonen et classification ascendante hiérarchique pour l’analyse de données géohistoriques / Ana-Maria Olteanu-Raimond in Revue internationale de géomatique, vol 25 n° 4 (octobre - décembre 2015)PermalinkClassification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkEfficient superpixel-level multitask joint sparse representation for hyperspectral image classification / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)Permalink