Descripteur
Termes IGN > mathématiques > statistique mathématique > analyse de données > classification > classification par réseau neuronal
classification par réseau neuronalVoir aussi |
Documents disponibles dans cette catégorie (382)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
![Tris disponibles](./images/orderby_az.gif)
Etendre la recherche sur niveau(x) vers le bas
Mask R-CNN and OBIA fusion improves the segmentation of scattered vegetation in very high-resolution optical sensors / Emilio Guirado in Sensors, vol 21 n° 1 (January 2021)
![]()
[article]
Titre : Mask R-CNN and OBIA fusion improves the segmentation of scattered vegetation in very high-resolution optical sensors Type de document : Article/Communication Auteurs : Emilio Guirado, Auteur ; Javier Blanco-Sacristán, Auteur ; Emilio Rodríguez-Caballero, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 320 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] arbuste
[Termes IGN] capteur optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de changement
[Termes IGN] image à très haute résolution
[Termes IGN] segmentation d'image
[Termes IGN] service écosystémique
[Termes IGN] surveillance de la végétation
[Termes IGN] zone arideRésumé : (auteur) Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands. Numéro de notice : A2021-157 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s21010320 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/s21010320 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97072
in Sensors > vol 21 n° 1 (January 2021) . - n° 320[article]
Titre : Mathematics and digital signal processing Type de document : Monographie Auteurs : Pavel Lyakhov, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 180 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-3-0365-1475-8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dégradation du signal
[Termes IGN] entropie maximale
[Termes IGN] filtre adaptatif
[Termes IGN] filtre numérique
[Termes IGN] modélisation 3D
[Termes IGN] qualité du signal
[Termes IGN] rapport signal sur bruit
[Termes IGN] signal numérique
[Termes IGN] transformation en ondelettesRésumé : (éditeur) Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems. Note de contenu : 1- Analysis of the quantization noise in discrete wavelet transform filters for 3D medical imaging
2- Maximum correntropy criterion based l1-iterative Wiener filter for sparse channel estimation robust to impulsive noise
3- Development of classification algorithms for the detection of postures using non-marker-based motion capture systems
4- Three-dimensional (3D) model-based lower limb stump automatic orientation
5- Improving calculation accuracy of digital filters based on finite field algebra
6- Multiresolution speech enhancement based on proposed circular nested microphone array in combination with sub-band affine projection algorithm
7- Classification of hydroacoustic signals based on harmonic wavelets and a deep learning artificial intelligence system
8- Quantification of the feedback regulation by digital signal analysis methods: Application to blood pressure control efficacy
9- Wood defect detection based on depth extreme learning machine
10- A division algorithm in a redundant residue number system using fractionsNuméro de notice : 28684 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-1475-8 En ligne : https://doi.org/10.3390/books978-3-0365-1475-8 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99983
Titre : Model and reality: Connecting BIM and the built environment Type de document : Thèse/HDR Auteurs : Gustaf Uggla, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 2021 Importance : 79 p. Format : 21 x 30 cm Note générale : bibliographie
Doctoral Thesis in Geodesy and Geoinformatics, KTH Royal Institute of Technology, StockholmLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données localisées 3D
[Termes IGN] format d'échange
[Termes IGN] format Industry foudation classes IFC
[Termes IGN] géoréférencement
[Termes IGN] métadonnées
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] projection Universal Transverse Mercator
[Termes IGN] qualité des données
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The adoption of building information modeling (BIM) in the architecture, engineering, and construction (AEC) industry is changing the way information regarding the built environment is created, stored, and exchanged. In short, documents are replaced with databases, processes are automated, and timelines become more circular with an emphasis on managing the life cycles of all manufactured objects. This has both direct and indirect consequences for the fields of geodesy and geographic information. Although geodesy and surveying have played a vital role in the construction process for a long time, new data standards and higher degrees of prefabrication and automation in the actual construction means that the topic of georeferencing must be revisited. In addition, using object oriented data structures means that semantic information must be inferred from geodata such as point clouds and images in order to adequately document existing assets. This thesis addresses the handling of 3D spatial information by analyzing different georeferencing methods and metadata used to describe the quality and characteristics of geodata. The outcomes include a recommendation for how the open BIM standard Industry Foundation Classes (IFC) could be extended to support more robust georeferencing, a suggestion that all standards and exchange formats used forthe built environment should include metadata for tolerance and uncertainty, and a framework that can describe characteristics of 3D spatial data that are not covered by conventional geographic metadata. On the semantic side, this thesis proposes an image-based method for identifying roadside objects in mobile laser scanning (MLS) point clouds, and it also explores the possibilities to train neural networks for point cloud segmentation by creating training data from 3D mesh models used in infrastructure design. Overall, the thesis describes the connection between model and reality, the importance of geodesy and geodetic surveying in this context, and makes contributions to both the geometric and semantic aspects of modeling the built environment. Note de contenu : 1- Introduction
2- Basis of knowledge and methods
3- Results
4- Summary of papersNuméro de notice : 28668 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Geodesy and Geoinformatics : KTH, Stockholm : 2021 DOI : sans En ligne : http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294087 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99878 Object detection using component-graphs and ConvNets with application to astronomical images / Thanh Xuan Nguyen (2021)
![]()
Titre : Object detection using component-graphs and ConvNets with application to astronomical images Type de document : Thèse/HDR Auteurs : Thanh Xuan Nguyen, Auteur ; Laurent Najman, Directeur de thèse ; Hugues Talbot, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2021 Importance : 175 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée en vue de l'obtention du Doctorat de l'Université Gustave Eiffel, Discipline InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de filtrage
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] image multibande
[Termes IGN] lissage de données
[Termes IGN] morphologie mathématique
[Termes IGN] théorie des graphesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This work investigates object detection algorithms with application to astronomical images. We specifically target to detect faint astronomical sources which value near the image background level. Our main directions include Mathematical Morphology (MM) and Convolutional Neural Network (ConvNet). The contributions of this study are presented in two parts:The first part proposes a novel morphological-based approach based on component-graphs and statistical hypothesis tests. The component-graphs can efficiently handle multi-band images while the statistical hypothesis tests can identify components that are significantly different from the background level. Beyond the classical component-trees and their multivariate extensions, the component-graph holds the complete structural information of multi-band images as directed acyclic graphs (DAGs). Such DAGs are more general and more powerful at the cost of non-trivial object filtering algorithms. Then, we introduce two algorithms to filter duplicated and partial components in the component-graphs. Experiments demonstrate that our proposed approach significantly improves object detection on both multi-band simulated and real astronomical images.The second part turns our attention to ConvNet direction.We introduce a real dataset of annotated astronomical objects.Based on this dataset, we propose two models: a ConvNet-based model and a hybrid model. The ConvNet-based model tailors astronomical contexts with three novel components, including a normalization layer, an object differentiation module, and a smoothness regularizer. Besides, the hybrid model uses both Morphology and ConvNet. In the hybrid method, morphological modules select region proposals while ConvNet extracts relevant information from the selected proposals. Ablation studies show that the two proposed models outperform the state of the art on both synthetic and real datasets. Note de contenu : Introduction
1- Object Detection in Astronomy
I- Mathematical morphology
2- Morphological Connected Operators
3- Object Detection with Component-graphs
II- ConvNet and morphology
4- ConvNet Object Detection Literature
5- ConvNet and Morphology
conclusions and perspectivesNuméro de notice : 15766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Université Gustave Eiffel : 2021 Organisme de stage : Laboratoire d'Informatique Gaspard-Monge DOI : sans En ligne : https://hal.science/tel-03622555v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100960 Panoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)
![]()
Titre : Panoptic segmentation of satellite image time series with convolutional temporal attention networks Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu
, Auteur
Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2021 Projets : 1-Pas de projet / Conférence : ICCV 2021, IEEE/CVF International Conference on Computer Vision 11/10/2021 17/10/2021 programme Importance : 17 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] contour
[Termes IGN] Pastis
[Termes IGN] Perceptron multicouche
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelleRésumé : (auteur) Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of agricultural parcels has major economic and environmental implications. While researchers have explored this problem for single images, we argue that the complex temporal patterns of crop phenology are better addressed with temporal sequences of images. In this paper, we present the first end-to-end, single-stage method for panoptic segmentation of Satellite Image Time Series (SITS). This module can be combined with our novel image sequence encoding network which relies on temporal self- attention to extract rich and adaptive multi-scale spatio- temporal features. We also introduce PASTIS, the first open- access SITS dataset with panoptic annotations. We demonstrate the superiority of our encoder for semantic segmentation against multiple competing architectures, and set up the first state-of-the-art of panoptic segmentation of SITS. Our implementation and PASTIS are publicly available. Numéro de notice : C2021-029 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.48550/arXiv.2107.07933 En ligne : https://doi.org/10.1109/ICCV48922.2021.00483 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98978 Perception de scène par un système multi-capteurs, application à la navigation dans des environnements d'intérieur structuré / Marwa Chakroun (2021)
PermalinkRegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)
PermalinkSemantic segmentation of sea ice type on Sentinel-1 SAR data using convolutional neural networks / Alissa Kouraeva (2021)
PermalinkStudy of an integrated pre-processing architecture for smart-imaging-systems, in the context of lowpower computer vision and embedded object detection / Luis Cubero Montealegre (2021)
PermalinkSuivi des vignes par télédétection de proximité : le deep learning au service de l’agriculture de précision / Sami Beniaouf (2021)
PermalinkSuper-resolution of VIIRS-measured ocean color products using deep convolutional neural network / Xiaoming Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
PermalinkSupplementary material for: Panoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)
PermalinkThe use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. Rukhovitch in Remote sensing, vol 13 n° 1 (January-1 2021)
PermalinkPermalinkUnderwater object detection and reconstruction based on active single-pixel imaging and super-resolution convolutional neural network / Mengdi Li in Sensors, vol 21 n° 1 (January 2021)
PermalinkUnifying remote sensing image retrieval and classification with robust fine-tuning / Dimitri Gominski (2021)
PermalinkCNN-based tree species classification using high resolution RGB image data from automated UAV observations / Sebastian Egli in Remote sensing, vol 12 n° 23 (December-2 2020)
PermalinkAutomatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)
PermalinkConvolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery / Teja Kattenborn in Remote sensing in ecology and conservation, vol 6 n° 4 (December 2020)
PermalinkA deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer / Xing Yan in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
PermalinkDeep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination / Frederik Hass in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)
PermalinkMapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks / Felix Schiefer in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)
PermalinkNonlocal graph convolutional networks for hyperspectral image classification / Lichao Mou in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
PermalinkA novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December-1 2020)
PermalinkParsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)
Permalink