Descripteur
Documents disponibles dans cette catégorie (1266)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Titre : Cross-year multi-modal image retrieval using siamese networks Type de document : Article/Communication Auteurs : Margarita Khokhlova , Auteur ; Valérie Gouet-Brunet
, Auteur ; Nathalie Abadie
, Auteur ; Liming Chen, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : ICIP 2020, 27th IEEE International Conference on Image Processing 25/10/2020 28/10/2020 Abou Dhabi Emirats Arabes Unis Proceedings IEEE Importance : pp 2361 - 2365 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] descripteur
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] réseau neuronal siamois
[Termes IGN] segmentation sémantiqueRésumé : (auteur) This paper introduces a multi-modal network that learns to retrieve by content vertical aerial images of French urban and rural territories taken about 15 years apart. This means it should be invariant against a big range of changes as the (nat-ural) landscape evolves over time. It leverages the original images and semantically segmented and labeled regions. The core of the method is a Siamese network that learns to extract features from corresponding image pairs across time. These descriptors are discriminative enough, such that a simple kNN classifier on top, suffices as final geo-matching criteria. The method outperformed SOTA "off-the-shelf" image descrip-tors GEM and ResNet50 on the new aerial images dataset. Numéro de notice : C2020-015 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP40778.2020.9190662 Date de publication en ligne : 01/10/2020 En ligne : https://doi.org/10.1109/ICIP40778.2020.9190662 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95684
Titre : Deep learning for remote sensing images with open source software Type de document : Guide/Manuel Auteurs : Rémi Cresson, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2020 Importance : 164 p. Présentation : Nombreuses illustrations en couleur ISBN/ISSN/EAN : 978-0-367-85848-3 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image radar
[Termes IGN] image Sentinel
[Termes IGN] jeu de données localisées
[Termes IGN] Orfeo Tool Box
[Termes IGN] QGIS
[Termes IGN] restauration d'image
[Termes IGN] segmentation sémantiqueIndex. décimale : 35.20 Traitement d'image Résumé : (Editeur) In today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource.This book is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data.
Specific Features of this Book:
- The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow)
- Presents approaches suited for real world images and data targeting large scale processing and GIS applications
- Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration)
- Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills.
- Includes deep learning techniques through many step by step remote sensing data processing exercises.Note de contenu :
Introduction
1. Backgrounds
2. Patch Based Classification
3. Semantic Segmentation
4. Image RestorationNuméro de notice : 26551 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Manuel DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97864 Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 26551-01 35.20 Livre Centre de documentation Télédétection Disponible
Titre : Deep learning for semantic feature extraction in aerial imagery Type de document : Thèse/HDR Auteurs : Ananya Gupta, Auteur ; Hujun Yin, Directeur de thèse ; Simon Watson, Directeur de thèse Editeur : Manchester [Royaume-Uni] : University of Manchester Année de publication : 2020 Importance : 151 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the faculty of Science and engineeringLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] cartographie d'urgence
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Dublin (Irlande ; ville)
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à très haute résolution
[Termes IGN] image aérienne
[Termes IGN] image multitemporelle
[Termes IGN] OpenStreetMap
[Termes IGN] réseau routier
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] voxelIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Remote sensing provides image and LiDAR data that can be useful for a number of tasks such as disaster mapping and surveying. Deep learning (DL) has been shown to provide good results in extracting knowledge from input data sources by the means of learning intermediate representation features. However, popular DL methods require large scaled datasets for training which are costly and time-consuming to obtain. This thesis investigates semantic knowledge extraction from remote sensing data using DL methods in regimes with limited labelled data. Firstly, semantic segmentation methods are compared and analysed on the task of aerial image segmentation. It is shown that pretraining on ImageNet improves the segmentation results despite the domain shift between ImageNet images and aerial images. A framework for mapping road networks in disaster struck areas is proposed. It uses pre and post disaster imagery and labels from OpenStreetMaps (OSM), forgoing the need for costly manually labelled data. Graph-based methods are used to update the pre-existing road maps from OSM. Experiments on a disaster dataset from Palu, Indonesia show the efficacy of the proposed method. A method for semantic feature extraction from aerial imagery is proposed which is shown to work well for multitemporal high resolution image registration. These feature are able to deal with temporal variations caused by seasonal changes. Methods for tree identification in LiDAR data have been proposed to overcome the need for manually labelled data. The first method works on high density point clouds and uses certain LiDAR data attributes for tree identification, achieving almost 90% accuracy. The second uses a voxel based 3D Convolutional Neural Network on low density LiDAR datasets and is able to identify most large trees. The third method is a scaled version of PointNet++ and achieves an F_score of 82.1 on the ISPRS benchmark, comparable to the state of the art methods but with increased efficiency. Finally, saliency methods used for explainability in image analysis are extended to work on 3D point clouds and voxel-based networks to help aid explainability in this area. It is shown that edge and corner features are deemed important by these networks for classification. These features are also demonstrated to be inherently sparse and pruned easily. Note de contenu : 1- Introduction
2- Background and Literature Review
3- Aerial Image Segmentation with Open Data
4- Aerial Image Registration
5- Tree Annotations in LiDAR Data
6- 3D Point Cloud Feature Explanations
7- Conclusions and Future WorkNuméro de notice : 28302 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Science and Engineering : University of Manchester : 2020 DOI : sans En ligne : https://www.research.manchester.ac.uk/portal/files/184627877/FULL_TEXT.PDF Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98051 Enhancing knowledge, skills, and spatial reasoning through location-based mobile learning / Christian Sailer (2020)
![]()
Titre : Enhancing knowledge, skills, and spatial reasoning through location-based mobile learning Type de document : Thèse/HDR Auteurs : Christian Sailer, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2020 Collection : Dissertationen ETH num. 26817 Importance : 289 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse géovisuelle
[Termes IGN] apprentissage (cognition)
[Termes IGN] enseignement secondaire
[Termes IGN] plateforme logicielle
[Termes IGN] programme interactif
[Termes IGN] raisonnement spatialRésumé : (auteur) Location-based mobile learning (LBML) involves learning in and about locations in order to explore, analyze, describe, and evaluate phenomena in authentic learning experiences and incorporate them into the real world. Learners gain an understanding of boththe immediate environment using all the senses and the spatial perception to create a holistic impression of the environmental phenomena. Mobile mapping technologies and location-based services are utilized to link learning contents to specific locations as stories, tasks, or assignments. A large number of research projects on mobile learning have investigated LBML systems and LBML approaches with regard to learner satisfaction, usability and efficiency. The projects achieved predominantly positive results regarding motivation and engagement but only fragmented results regarding the cognitive learning outcomes.This dissertation describes how an LBML system utilizing GIS technology enhances the educational learning outcomes with a special focus on the spatial thinking process. Furthermore, this dissertation describes novel approaches of visual analytics with 2D and 3D map web components to produce new teaching strategies during the activities and new metacognitive strategies to evaluate and reflect the activity. The study presented in this dissertation covers case studies in universities, vocational schools, and informal education environments using design-based research to develop a mobile-friendly interactive mapping platform. Furthermore, the platform includes multimedia capabilities and interfaces to connect external services and content. The main study was conducted in a secondary school under real conditions and evaluated the technology regarding the learning performance and the teaching activities before, during, and after the activity. The results reveal a better cognitive learning outcome in classroom exams when a teaching sequence of several weeks includes an outdoor activity of a double lesson. Moreover, there is potential for enhancing learning beyond the outdoor part to improve spatial reasoning. Long-term self-assessment of the learners, however, resulted in no impact, whether cognitively or affectively. The workload for outdoor teaching compared to classroom teaching is higher mainly due to the profound inspections of the location. The findings and their implications for research and teacher education were discussed in order to corroborate the educational value of LBML to motivate educators using LBML strategies for teaching. Numéro de notice : 17657 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : doctoral thesis : Geomatics : ETH Zurich : 2020 En ligne : http://dx.doi.org/10.3929/ethz-b-000458558 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97924
Titre : Epipolar rectification of a generic camera Type de document : Article/Communication Auteurs : Marc Pierrot-Deseilligny , Auteur ; Ewelina Rupnik
, Auteur
Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2020 Importance : 19 p. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] caméra numérique
[Termes IGN] capteur en peigne
[Termes IGN] compensation par faisceaux
[Termes IGN] correction d'image
[Termes IGN] couple stéréoscopique
[Termes IGN] courbe épipolaire
[Termes IGN] géométrie épipolaire
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] orthorectification
[Termes IGN] points homologues
[Termes IGN] projection
[Termes IGN] vision par ordinateur
[Termes IGN] vue perspectiveRésumé : (Auteur) We propose a generic method for epipolar resampling that is not tied to a specific camera model. We demonstrate the effectiveness of the approach on a central perspective, pushbroom and pushbroom panoramic camera models. We also devise an epipolarability index that measures the suitability of an image pair for epipolar rectification, and provide a formal derivation of the ambiguity bound to epipolar resampling. Numéro de notice : P2020-010 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/MATHEMATIQUE Nature : Preprint nature-HAL : Préprint DOI : sans Date de publication en ligne : 15/10/2020 En ligne : https://hal.science/hal-02968078 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96096 Documents numériques
en open access
Epipolar rectification of a generic camera - pdf preprint v1Adobe Acrobat PDFEstimation of metabolic flows of urban environment based on fuzzy expert knowledge / Igor Patrakeyev in Geodesy and cartography, vol 46 n° 1 (January 2020)
PermalinkÉtude préalable à la mise en oeuvre de la qualification des contributions dans les bases de données collaboratives hébergées par l’IGN / Lilian Calas (2020)
PermalinkExtraction de connaissances pour la description de l'environnement maritime côtier à partir de textes d'aide à la navigation / Léa Lamotte in Revue des Nouvelles Technologies de l'Information, E.36 (2020)
PermalinkPermalinkGénération de cartes tactiles photoréalistes pour personnes déficientes visuelles par apprentissage profond / Gauthier Fillières-Riveau in Revue internationale de géomatique, vol 30 n° 1-2 (janvier - juin 2020)
PermalinkPermalinkGlobal investigation of marine atmospheric boundary layer rolls using Sentinel-1 SAR data / Chen Wang (2020)
PermalinkDe l’image optique "multi-stéréo" à la topographie très haute résolution et la cartographie automatique des failles par apprentissage profond / Lionel Matteo (2020)
PermalinkPermalinkImaging and diagnostic of sub-wavelength micro-structures, from closed-form algorithms to deep learning / Peipei Ran (2020)
PermalinkPermalinkINS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)
PermalinkPermalinkPermalinkInteractions between hierarchical learning and visual system modeling : image classification on small datasets / Thalita Firmo Drumond (2020)
PermalinkPermalinkPermalinkPermalinkLearning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)
PermalinkPermalinkPermalinkPermalinkPermalinkModélisation sémantique et programmation générative pour une simulation multi-agent dans le contexte de gestion de catastrophe / Claire Prudhomme in Revue internationale de géomatique, vol 30 n° 1-2 (janvier - juin 2020)
PermalinkPermalinkPermalinkA new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods / Yongjiu Feng in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)
PermalinkNonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou (2020)
PermalinkOn the adjustment, calibration and orientation of drone photogrammetry and laser-scanning / Emmanuel Clédat (2020)
PermalinkPermalinkPast and future evolution of French Alpine glaciers in a changing climate: a deep learning glacio-hydrological modelling approach / Jordi Bolibar Navarro (2020)
PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkSatellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)
![]()
PermalinkSmoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors / Paul Chauchat (2020)
PermalinkPermalinkSpatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)
PermalinkPermalink