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imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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Multi-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot (2021)
Titre : Multi-modal temporal attention models for crop mapping from satellite time series Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu , Auteur ; Nesrine Chehata , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2021 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] base de données d'images
[Termes IGN] carte agricole
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] Pastis
[Termes IGN] segmentation d'imageRésumé : (auteur) Optical and radar satellite time series are synergetic: optical images contain rich spectral information, while C-band radar captures useful geometrical information and is immune to cloud cover. Motivated by the recent success of temporal attention-based methods across multiple crop mapping tasks, we propose to investigate how these models can be adapted to operate on several modalities. We implement and evaluate multiple fusion schemes, including a novel approach and simple adjustments to the training procedure, significantly improving performance and efficiency with little added complexity. We show that most fusion schemes have advantages and drawbacks, making them relevant for specific settings. We then evaluate the benefit of multimodality across several tasks: parcel classification, pixel-based segmentation, and panoptic parcel segmentation. We show that by leveraging both optical and radar time series, multimodal temporal attention-based models can outmatch single-modality models in terms of performance and resilience to cloud cover. To conduct these experiments, we augment the PASTIS dataset with spatially aligned radar image time series. The resulting dataset, PASTIS-R, constitutes the first large-scale, multimodal, and open-access satellite time series dataset with semantic and instance annotations. Numéro de notice : P2021-005 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : sans Date de publication en ligne : 14/12/2021 En ligne : https://arxiv.org/abs/2112.07558v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99392
Titre : Multispectral object detection Type de document : Thèse/HDR Auteurs : Heng Zhang, Auteur ; Elisa Fromont, Directeur de thèse ; Sébastien Lefèvre, Directeur de thèse Editeur : Rennes : Université de Rennes 1 Année de publication : 2021 Importance : 114 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse présentée en vue de l’obtention du grade de docteur en Informatique de l'Université de Rennes 1Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] chambre de prise de vue thermique
[Termes IGN] détection d'objet
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] efficacité
[Termes IGN] fusion de données multisource
[Termes IGN] image multibande
[Termes IGN] précision de la classification
[Termes IGN] qualité du modèle
[Termes IGN] segmentation sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Only using RGB cameras for automatic outdoor scene analysis is challenging when, for example, facing insufficient illumination or adverse weather. To improve the recognition reliability, multispectral systems add additional cameras (e.g. infra-red) and perform object detection from multispectral data. Although multispectral scene analysis with deep learning has been shown to have a great potential, there are still many open research questions and it has not been widely deployed in industrial contexts. In this thesis, we investigated three main challenges about multispectral object detection: (1) the fast and accurate detection of objects of interest from images; (2) the dynamic and adaptive fusion of information from different modalities;(3) low-cost and low-energy multispectral object detection and the reduction of its manual annotation efforts. In terms of the first challenge, we first optimize the label assignment of the object detection training with a mutual guidance strategy between the classification and localization tasks; we then realize an efficient compression of object detection models including the teacher-student prediction disagreements in a feature-based knowledge distillation framework. With regard to the second challenge, three different multispectral feature fusion schemes are proposed to deal with the most difficult fusion cases where different cameras provide contradictory information. For the third challenge, a novel modality distillation framework is firstly presented to tackle the hardware and software constraints of current multispectral systems; then a multi-sensor-based active learning strategy is designed to reduce the labeling costs when constructing multispectral datasets. Note de contenu : 1. Introduction
1.1 Context and motivations
1.2 Thesis outline
2. Deep learning background
2.1 General object detection
2.2 Multispectral object detection
2.3 Knowledge distillation
2.4 Active learning
2.5 Datasets
3. Efficient object detection on embedded devices
3.1 Best practices for training object detection models
3.2 Mutual Guidance for Anchor Matching
3.3 Prediction Disagreement aware Feature Distillation
3.4 Experimental results
4. Information fusion from multispectral data
4.1 Multispectral Fusion with Cyclic Fuse-and-Refine
4.2 Progressive Spectral Fusion
4.3 Experimental results for CFR and PS-Fuse
4.4 Guided Attentive Feature Fusion
4.5 Experimental results for GAFF
5. Sensors and annotations: low cost multispectral data processing
5.1 Deep Active Learning from Multispectral Data
5.2 Low-cost Multispectral Scene Analysis with Modality Distillation
6. Conclusions and future works
6.1 Conclusions
6.2 Application to remote sensing data
6.3 PerspectivesNuméro de notice : 26765 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Rennes 1 : 2021 Organisme de stage : (IRISA) INRIA nature-HAL : Thèse DOI : sans Date de publication en ligne : 17/01/2022 En ligne : https://hal.science/tel-03530257/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99855 Near-real-time identification of the drivers of deforestation in French Guiana / Marie Ballère (2021)
Titre : Near-real-time identification of the drivers of deforestation in French Guiana Type de document : Article/Communication Auteurs : Marie Ballère , Auteur ; Stéphane Mermoz, Auteur ; Alexandre Bouvet, Auteur ; Thierry Koleck, Auteur Editeur : Munich [Allemagne] : European Geosciences Union EGU Année de publication : 2021 Conférence : EGU 2021, General Assembly 19/04/2021 30/04/2021 en ligne OA Abstracts only Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] déboisement
[Termes IGN] exploitation forestière
[Termes IGN] forêt tropicale
[Termes IGN] Guyane (département français)
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] mine d'or
[Termes IGN] surveillance forestière
[Termes IGN] urbanisationNuméro de notice : C2021-004 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans En ligne : https://doi.org/10.5194/egusphere-egu21-16015 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97598 A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network / Wang Li in Advances in space research, vol 67 n° 1 (January 2021)
[article]
Titre : A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network Type de document : Article/Communication Auteurs : Wang Li, Auteur ; Changyong He , Auteur ; Andong Hu, Auteur ; Dongsheng Zhao, Auteur ; Yi Shen, Auteur ; Kefei Zhang, Auteur Année de publication : 2021 Article en page(s) : pp 20 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] correction ionosphérique
[Termes IGN] image Formosat/COSMIC
[Termes IGN] modèle ionosphérique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] teneur totale en électronsRésumé : (auteur) There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde’s observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde’s data in the polar regions. The differences between ANN-based maps and references show that the discrepancies between two ionospheric detecting techniques are proportional to the intensity of solar radiation. Besides, a new method based on the ANN technique is proposed to reduce the discrepancies for improving ionospheric models developed by multiple measurements, the results indicate that the RMSEs of ANN models optimized by the method are 14–25% lower than the models without the application of the method. Furthermore, the ionospheric model built by the multiple measurements with the application of the method is more powerful in capturing the ionospheric dynamic physics features, such as equatorial ionization, Weddell Sea, mid-latitude summer nighttime and winter anomalies. In conclusion, the new method is significant in improving the accuracy and physical characteristics of an ionospheric model based on multi-source observations. Numéro de notice : A2021-986 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2020.07.032 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1016/j.asr.2020.07.032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102912
in Advances in space research > vol 67 n° 1 (January 2021) . - pp 20 - 34[article]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 PermalinkOptimisation et développement des solutions photogrammétriques pour la réalisation des relevés de façade au sein du cabinet ELLIPSE Géomètres-Experts / Guillaume Jeannin (2021)PermalinkOptimisation des protocoles de numérisation 3D multi-capteurs et de fusion de données hétérogènes au sein de l'entreprise Premier plan / Elisa Gautron (2021)PermalinkPerception de scène par un système multi-capteurs, application à la navigation dans des environnements d'intérieur structuré / Marwa Chakroun (2021)PermalinkPlanimetric simplification and lexicographic optimal chains for 3D urban scene reconstruction / Julien Vuillamy (2021)PermalinkPermalinkPermalinkPermalinkProduction et mise à jour d’un produit BD Forêt V3 par apprentissage profond / Sébastien Giordano (2021)PermalinkProgrammation d’un système de scannage multiple pilotable et mise en place de tests de qualité pour l’optimisation d’une chaîne de traitement photogrammétrique / Augustin Cosson (2021)PermalinkProposition d’un référentiel de description et de détection de la végétation dans une agglomération / Mathilde Segaud (2021)PermalinkQualification des données LiDAR GEDI pour le suivi de l’impact climatique sur la forêt de Südharz / Iris Jeuffrard (2021)PermalinkRapport d'activité 2020 de l'Institut National de l'Information Géographique et Forestière IGN, 1. 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