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Titre : Processing and analysis of hyperspectral data Type de document : Monographie Auteurs : Jie Chen, Éditeur scientifique ; Yingying Song, Éditeur scientifique ; Hengchao Li, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 140 p. ISBN/ISSN/EAN : 978-1-78985-109-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] image à haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] image proche infrarouge
[Termes IGN] qualité des eaux
[Termes IGN] signature spectrale
[Termes IGN] turbidité des eauxRésumé : (Editeur) Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods. Note de contenu : Section One - Theoretical advances of hyperspectral image processing
Chapter 1 - Hyperspectral endmember extraction techniques
Chapter 2 - Hyperspectral image classification
Chapter 3 - Hyperspectral image super-resolution using optimization and DCNN-based methods
Chapter 4 - Fast chaotic encryption for hyperspectral images
Section Two - Applications of hyperspectral image processing
Chapter 5 - NIR hyperspectral imaging for mapping of moisture content distribution in tea buds during dehydration
Chapter 6 - Use of hyperspectral remote sensing to estimate water qualityNuméro de notice : 26560 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.78179 En ligne : http://doi.org/10.5772/intechopen.78179 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98243
Titre : Recent advances in geographic information system for Earth sciences Type de document : Monographie Auteurs : Yosoon Choi, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 264 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-490-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie thématique
[Termes IGN] codage
[Termes IGN] cognition
[Termes IGN] développement durable
[Termes IGN] effondrement de terrain
[Termes IGN] interface homme-machine
[Termes IGN] modèle dynamique
[Termes IGN] modèle numérique de surface
[Termes IGN] oculométrie
[Termes IGN] outil d'aide à la décision
[Termes IGN] planification urbaine
[Termes IGN] QGIS
[Termes IGN] réseau de transport
[Termes IGN] réseau social
[Termes IGN] utilisation du solRésumé : (éditeur) Geographic information systems (GISs) have played a vital role in Earth sciences by providing a powerful means of observing the world and various tools for solving complex problems. The scientific community has used GISs to reveal fascinating details about the Earth and other planets. This book on recent advances in GIS for Earth sciences includes 12 publications from esteemed research groups worldwide. The research and review papers in this book belong to the following broad categories: Earth science informatics (geoinformatics), mining, hydrology, natural hazards, and society. Note de contenu : 1- Recent advances in geographic information system for earth sciences
2- An efficient parallel algorithm for polygons overlay analysis
3- Vector map random encryption algorithm based on multi-scale simplification and Gaussian distribution
4- Evaluation of effective cognition for the QGIS processing modeler
5- Geo-sensor framework and composition toolbox for efficient deployment of multiple spatial context service platforms in sensor networks
6- Review of GIS-based applications for mining: Planning, operation, and environmental management
7- A tightly coupled GIS and spatiotemporal modeling for methane emission simulation in the underground coal mine system
8- Evaluation of reliable digital elevation model resolution for TOPMODEL in two mountainous watersheds, South Korea
9- Spatiotemporal changes of urban rainstorm-related micro-blogging activities in response to rainstorms: A case study in Beijing, China
10- Rainfall induced landslide studies in Indian Himalayan region: A critical review
11- GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques
12- Spatiotemporal dynamics and obstacles of the multi-functionality of land use in Xiangxi, China
13- Analyzing spatial community pattern of network traffic flow and its variations across time based on taxi GPS trajectoriesNuméro de notice : 28439 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-490-9 En ligne : https://doi.org/10.3390/books978-3-03936-490-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98876
Titre : Recent trends in artificial neural networks Type de document : Monographie Auteurs : Ali Sadollah, Éditeur scientifique ; Carlos M. Travieso-Gonzalez, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 150 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-78985-859-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] logique floue
[Termes IGN] réseau neuronal artificielRésumé : (éditeur) Artificial intelligence (AI) is everywhere and it's here to stay. Most aspects of our lives are now touched by artificial intelligence in one way or another, from deciding what books or flights to buy online to whether our job applications are successful, whether we receive a bank loan, and even what treatment we receive for cancer. Artificial Neural Networks (ANNs) as a part of AI maintains the capacity to solve problems such as regression and classification with high levels of accuracy. This book aims to discuss the usage of ANNs for optimal solving of time series applications and clustering. Bounding of optimization methods particularly metaheuristics considered as global optimizers with ANNs make a strong and reliable prediction tool for handling real-life application. This book also demonstrates how different fields of studies utilize ANNs proving its wide reach and relevance. Note de contenu : 1- Time series from clustering: An approach to forecast crime patterns
2- Encountered problems of time series with neural networks: Models and architectures
3- Metaheuristics and artificial neural networks
4- An improved algorithm for optimising the production of biochemical systems
5- Object recognition using convolutional neural networks
6- Prediction of wave energy potential in India: A fuzzy-ANN approach
7- Deep learning training and benchmarks for Earth observation images: Data sets, features, and procedures
8- Data mining technology for structural control systems: Concept, development, and comparisonNuméro de notice : 28497 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.77409 En ligne : https://doi.org/10.5772/intechopen.77409 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99247
Titre : Remote sensing based building extraction Type de document : Monographie Auteurs : Mohammad Awrangjeb, Auteur ; Xiangyun Hu, Auteur ; Bisheng Yang, Auteur ; Jiaojiao Tian, Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 442 p. ISBN/ISSN/EAN : 978-3-03928-383-5 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image à haute résolution
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Editeur) Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D. Numéro de notice : 26305 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03928-383-5 Date de publication en ligne : 07/04/2020 En ligne : https://doi.org/10.3390/books978-3-03928-383-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95064
Titre : Representation learning for natural language processing Type de document : Monographie Auteurs : Zhiyuan Liu, Éditeur scientifique ; Yankai Lin, Éditeur scientifique ; Maosong Sun, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Importance : 334 p. ISBN/ISSN/EAN : 978-981-1555732-- Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] données massives
[Termes IGN] exploration de données
[Termes IGN] représentation des connaissances
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau sémantique
[Termes IGN] traitement du langage naturelRésumé : (Editeur) This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. Note de contenu :
1. Representation Learning and NLP
1.1 Motivation
1.2 Why Representation Learning Is Important for NLP
1.3 Basic Ideas of Representation Learning
1.4 Development of Representation Learning for NLP
1.5 Learning Approaches to Representation Learning for NLP
1.6 Applications of Representation Learning for NLP
1.7 The Organization of This Book
2. Word Representation
2.1 Introduction
2.2 One-Hot Word Representation
2.3 Distributed Word Representation
2.4 Contextualized Word Representation
2.5 Extensions
2.6 Evaluation
3. Compositional Semantics
3.1 Introduction
3.2 Semantic Space
3.3 Binary Composition
3.4 N-Ary Composition
4. Sentence Representation
4.1 Introduction
4.2 One-Hot Sentence Representation
4.3 Probabilistic Language Model
4.4 Neural Language Model
4.5 Applications
5. Document Representation
5.1 Introduction
5.2 One-Hot Document Representation
5.3 Topic Model
5.4 Distributed Document Representation
5.5 Applications
6. Sememe Knowledge Representation
6.1 Introduction
6.2 Sememe Knowledge Representation
6.3 Applications
7. World Knowledge Representation
7.1 Introduction
7.2 Knowledge Graph Representation
7.3 Multisource Knowledge Graph Representation
7.4 Applications
8. Network Representation
8.1 Introduction
8.2 Network Representation
8.3 Graph Neural Networks
9. Cross-Modal Representation
9.1 Introduction
9.2 Cross-Modal Representation
9.3 Image Captioning
9.4 Visual Relationship Detection
9.5 Visual Question Answering
10. Resources
10.1 Open-Source Frameworks for Deep Learning
10.2 Open Resources for Word Representation
10.3 Open Resources for Knowledge Graph Representation
10.4 Open Resources for Network Representation
10.5 Open Resources for Relation Extraction
11. OutlookNuméro de notice : 26515 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.1007/978-981-15-5573-2 En ligne : http://doi.org/10.1007/978-981-15-5573-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97296 PermalinkSuperpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
PermalinkSystème de traitement d’images temps réel dédié à la mesure de champs denses de déplacements et de déformations / Seyfeddine Boukhtache (2020)
PermalinkTorch-Points3D: A modular multi-task framework for reproducible deep learning on 3D point clouds / Thomas Chaton (2020)
PermalinkUnderwater field equipment of a network of landmarks optimized for automatic detection by AI / Laurent Beaudoin (2020)
PermalinkUnsupervised satellite image time series analysis using deep learning techniques / Ekaterina Kalinicheva (2020)
PermalinkVers une occupation du sol France entière par imagerie satellite à très haute résolution / Tristan Postadjian (2020)
PermalinkPermalinkShip identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)
PermalinkCombining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)
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