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Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ / Zhimin Wang in Computers & geosciences, vol 158 (January 2022)  

Public [article]  inComputers & geosciences > vol 158 (January 2022) . - n° 104969 Titre : Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ Type de document : Article/Communication Auteurs : Zhimin Wang, Auteur ; Jiasheng Wang, Auteur ; Kun Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104969 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classe sémantique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image à haute résolution [Termes IGN] image Gaofen [Termes IGN] raisonnement sémantique [Termes IGN] segmentation sémantique Résumé : (auteur) Aiming at solving the problems of inaccurate segmentation of edge targets, inconsistent segmentation of different types of targets, and slow prediction efficiency on semantic segmentation of high-resolution remote sensing images by classical semantic segmentation network, this study proposed a class feature attention mechanism fused with an improved Deeplabv3+ network called CFAMNet for semantic segmentation of common features in remote sensing images. First, the correlation between classes is enhanced using the class feature attention module to extract and process different categories of semantic information better. Second, the multi-parallel atrous spatial pyramid pooling structure is used to enhance the correlation between spaces, to extract the context information of different scales of an image better. Finally, the encoder-decoder structure is used to refine the segmentation results. The segmentation effect of the proposed network is verified by experiments on the public data set GaoFen image dataset (GID). The experimental results show that the CFAMNet can achieve the mean intersection over union (MIOU) and overall accuracy (OA) of 77.22% and 85.01%, respectively, on the GID, thus surpassing the current mainstream semantic segmentation networks. Numéro de notice : A2022-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104969 Date de publication en ligne : 26/10/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104969 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99269 [article]

A hydrological sensor web ontology based on the SSN ontology: A case study for a flood / Chao Wang in ISPRS International journal of geo-information, vol 7 n° 1 (January 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 1 (January 2018) Titre : A hydrological sensor web ontology based on the SSN ontology: A case study for a flood Type de document : Article/Communication Auteurs : Chao Wang, Auteur ; Nengcheng Chen, Auteur ; Wei Wang, Auteur ; Zeqiang Chen, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] classe d'objets [Termes IGN] données spatiotemporelles [Termes IGN] hydrographie [Termes IGN] modèle d'ontologie [Termes IGN] ontologie [Termes IGN] raisonnement sémantique [Termes IGN] réseau de capteurs Résumé : (Auteur) Accompanying the continuous development of sensor network technology, sensors worldwide are constantly producing observation data. However, the sensors and their data from different observation platforms are sometimes difficult to use collaboratively in response to natural disasters such as floods for the lack of semantics. In this paper, a hydrological sensor web ontology based on SSN ontology is proposed to describe the heterogeneous hydrological sensor web resources by importing the time and space ontology, instantiating the hydrological classes, and establishing reasoning rules. This work has been validated by semantic querying and knowledge acquiring experiments. The results demonstrate the feasibility and effectiveness of the proposed ontology and its potential to grow into a more comprehensive ontology for hydrological monitoring collaboratively. In addition, this method of ontology modeling is generally applicable to other applications and domains. Numéro de notice : A2018-039 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7010002 En ligne : https://doi.org/10.3390/ijgi7010002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89261 [article]

Introduction to Artificial Intelligence / Wolfgang Ertel (2017)  

Public Titre : Introduction to Artificial Intelligence Type de document : Monographie Auteurs : Wolfgang Ertel, Auteur Editeur : Springer International Publishing Année de publication : 2017 Importance : 365 p. ISBN/ISSN/EAN : 978-3-319-58487-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] classification bayesienne [Termes IGN] entropie maximale [Termes IGN] exploration de données [Termes IGN] PROLOG [Termes IGN] raisonnement sémantique [Termes IGN] réseau neuronal artificiel Résumé : (éditeur) This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website. Note de contenu : 1- Introduction 2- Propositional Logic 3- First-order Predicate Logic 4- Limitations of Logic 5- Logic Programming with PROLOG 6- Search, Games and Problem Solving 7- Reasoning with Uncertainty 8- Machine Learning and Data Mining 9- Neural Networks 10- Reinforcement Learning 11- Solutions for the Exercises Numéro de notice : 25753 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-58487-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94945