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Titre : Satellite systems : design, modeling, simulation and analysis Type de document : Monographie Auteurs : Tien M. Nguyen, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 ISBN/ISSN/EAN : 978-1-83968-374-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Technologies spatiales
[Termes IGN] compression d'image
[Termes IGN] données GNSS
[Termes IGN] données spatiotemporelles
[Termes IGN] image à haute résolution
[Termes IGN] image optique
[Termes IGN] microsatellite
[Termes IGN] télédétection spatiale
[Termes IGN] température de surface de la mer
[Termes IGN] véhicule spatialIndex. décimale : 21.00 Technologies spatiales Résumé : (Editeur) This book provides a high-level overview of the current state of the art and future of satellite systems, satellite control systems, and satellite systems design. Chapters cover such topics as existing and future satellite systems, satellite communication subsystems, space control and Space Situation Awareness (SAA), machine learning methods with novel neural networks, data measurements in Global Navigation Satellite Systems, and much more. This volume is a practical reference for system engineers, design engineers, system analysts, and researchers in satellite engineering and advanced mathematical modeling fields. Note de contenu : 1. Communication Subsystems for Satellite Design / By Hung H. Nguyen and Peter S. Nguyen
2. Overview of Existing and Future Advanced Satellite Systems / By John Nguyen
3. Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors / By Dan Shen, Carolyn Sheaff, Genshe Chen, Jingyang Lu, Mengqing Guo, Erik Blasch and Khanh Pham
4. Future Satellite System Architectures and Practical Design Issues: An Overview / By Tien M. Nguyen
5. System Designs of Microsatellites: A Review of Two Schools of Thoughts / By Triharjanto Robertus
6. Design of Intelligent and Open Avionics System Onboard / By Changqing Wu, Xiaodong Han and Yakun Wang
7. Dynamic Link from Liftoff to Final Orbital Insertion for a MEO Space Vehicle / By Jack K. Kreng and Gleason Q. Chen
8. Effective Algorithms for Detection Outliers and Cycle Slip Repair in GNSS Data Measurements / By Igor V. Bezmenov
9. Analysis of Spatiotemporal Variability of Surface Temperature of Okhotsk Sea and Adjacent Waters Using Satellite Data / By Dmitry Lozhkin
10. Compression of High-Resolution Satellite Images Using Optical Image Processing / By Anirban Patra, Arijit Saha, Debasish Chakraborty and Kallol BhattacharyaNuméro de notice : 26712 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.73789 Date de publication en ligne : 14/04/2021 En ligne : https://doi.org/10.5772/intechopen.73789 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99491 Steps-based tree crown delineation by analyzing local minima for counting the trees in very high resolution satellite imagery / Debasish Chakraborty in Geocarto international, vol 36 n° 1 ([01/01/2021])
[article]
Titre : Steps-based tree crown delineation by analyzing local minima for counting the trees in very high resolution satellite imagery Type de document : Article/Communication Auteurs : Debasish Chakraborty, Auteur ; Pranshu Kumar, Auteur Année de publication : 2021 Article en page(s) : pp 110 - 120 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arborescence
[Termes IGN] comptage
[Termes IGN] détection de contours
[Termes IGN] houppier
[Termes IGN] image à haute résolution
[Termes IGN] image à très haute résolution
[Termes IGN] image satellite
[Termes IGN] image Worldview
[Termes IGN] itération
[Termes IGN] segmentation d'imageRésumé : (Auteur) In this study primarily, high-resolution (HR) satellite image is segmented into tree and non-tree regions. Thereafter plots the local minima in the segmented image. Point surrounded by the higher intensity values is called as local minima. The local minimum is the starting point for marking the tree crown boundary. The adjacent darker points along the local minima are plotted in a specific direction for marking the tree crown boundary. Subsequently a seven steps iterative procedure is followed for delineating and counting the tree crowns. The validation of the method is done on WorldView-2 data. Numéro de notice : A2021-054 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1611947 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1611947 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96779
in Geocarto international > vol 36 n° 1 [01/01/2021] . - pp 110 - 120[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2021011 RAB Revue Centre de documentation En réserve L003 Disponible The challenge of robust trait estimates with deep learning on high resolution RGB images / Etienne David (2021)
Titre : The challenge of robust trait estimates with deep learning on high resolution RGB images Type de document : Thèse/HDR Auteurs : Etienne David, Auteur ; Frédéric Baret, Directeur de thèse Editeur : Avignon : Université d'Avignon Année de publication : 2021 Importance : 145 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université d'Avignon, spécialité Sciences AgronomiquesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'images
[Termes IGN] blé (céréale)
[Termes IGN] céréales
[Termes IGN] comptage
[Termes IGN] cultures
[Termes IGN] densité de la végétation
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] jeu de données
[Termes IGN] surveillance agricoleIndex. décimale : THESE Thèses et HDR Résumé : (auteur) High throughput plant phenotyping, especially in the context of open field acquisitions, relies on the interpretation of data from different sensors implemented on various vectors such as tractors, robots or drones. Initially, these data were interpreted using remote sensing algorithms that exploit the spatial resolution of the signal. Since 2015, however, progresses of ”Deep Learning”, based on the training on examples, has already obtained promising results for measuring the rate of cover, counting plants or organs. It uses learned convolution layers, can take advantage of the spatial organization of the signal. The advantage of these methods is that they are based on Red-Green-Blue (RGB) sensors, which are much less expensive than multi- or hyperspectral imagers. However, these methods are sensitive to changes in the distribution between the data used in training and the predicted data. In practice, variable prediction errors from site to site can be observed using these methods. The objective of the thesis is to understand the causes of these variations and propose solutions for reliable phenotypic trait estimates using Deep Learning. The study focuses on detecting plants and organs from high-resolution RGB images acquired in the field. Our work first focused on the constitution of diversified image databases from different locations and stages of development for plant emergence (maize, beet, sunflower) and wheat ears, which allowed the publication of two annotated databases, grouping 27 acquisition sessions for thedrone and 47 for the ear detection. The datasets demonstrate the performances difference between the published results and ours due to the change in distribution. To go beyond the limits of the usual methods, we organized two data competitions, the Global Wheat Challenges, in 2020 and 2021, which allowed us to obtain solutions trained for robustness on a different data set than the training one. The analysis of the solutions showed the importance of the training strategies for robustness beyond the architectures used. We have also shown that these solutions can be effectively deployed as a replacement for manual counting. Finally, we have demonstrated the inefficiency of training functions designed for robust training. Our work opens the prospect of a better evaluation of Deep Learning in the context of high-throughput phenotyping and thus of confidence in its use in real-life conditions. Note de contenu : 1- Introduction
2- Evaluation of the robustness of handcrafted and deep learning methods for plant density estimation
3- Design of a large and diverse dataset for training and evaluating deep learning models: application to wheat head detection
4- Competition design to train robust Deep Learn model: the example of the Global Wheat Challenges
5- GlobalWheat-Wilds: Global Wheat Head Dataset as a benchmark of in-the-wild distribution shifts
6- Conclusion and perspectivesNuméro de notice : 15244 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences Agronomiques : Avignon : 2021 Organisme de stage : Laboratoire EMMAH DOI : sans En ligne : https://hal.inrae.fr/tel-03431192v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100610 Automatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)
[article]
Titre : Automatic building footprint extraction from UAV images using neural networks Type de document : Article/Communication Auteurs : Zoran Kokeza, Auteur ; Miroslav Vujasinović, Auteur ; Miro Govedarica, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 545 - 561 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] cartographie cadastrale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection du bâti
[Termes IGN] empreinte
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] modèle numérique de surface
[Termes IGN] orthoimage
[Termes IGN] zone d'intérêtRésumé : (Auteur) Up-to-date cadastral maps are crucial for urban planning. Creating those maps with the classical geodetic methods is expensive and time-consuming. Emerge of Unmanned Aerial Vehicles (UAV) made a possibility for quick acquisition of data with much more details than it was possible before. The topic of the research refers to the challenges of automatic extraction of building footprints on high-resolution orthophotos. The objectives of this study were as follows: (1) to test the possibility of using different publicly available datasets (Tanzania, AIRS and Inria) for neural network training and then test the generalisation capability of the model on the Area Of Interest (AOI); (2) to evaluate the effect of the normalised digital surface model (nDSM) on the results of neural network training and implementation. Evaluation of the results shown that the models trained on the Tanzania (IoU 36.4%), AIRS (IoU 64.4%) and Inria (IoU 7.4%) datasets doesn't satisfy the requested accuracy to update cadastral maps in study area. Much better results are achieved in the second part of the study, where the training of the neural network was done on tiles (256x256) of the orthophoto of AOI created from data acquired using UAV. A combination of RGB orthophoto with nDSM resulted in a 2% increase of IoU, achieving the final IoU of over 90%. Numéro de notice : A2020-777 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2020.04.545-561 Date de publication en ligne : 26/10/2020 En ligne : http://www.geodetski-vestnik.com/en/2020-4 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96706
in Geodetski vestnik > vol 64 n° 4 (December 2020 - February 2021) . - pp 545 - 561[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 139-2020041 RAB Revue Centre de documentation En réserve L003 Disponible Mapping 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)
[article]
Titre : Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks Type de document : Article/Communication Auteurs : Felix Schiefer, Auteur ; Teja Kattenborn, Auteur ; Annett Frick, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 205-215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] arbre (flore)
[Termes IGN] carte forestière
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espèce végétale
[Termes IGN] Forêt-Noire, massif de la
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier local
[Termes IGN] segmentation sémantique
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods for the mapping of forest tree species do not exploit the respective, rich spatial information. Here, we assessed the potential of convolutional neural networks (CNNs) and very high-resolution RGB imagery from UAVs for the mapping of tree species in temperate forests. We used multicopter UAVs to obtain very high-resolution ( Numéro de notice : A2020-706 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.015 Date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.015 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96236
in ISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 205-215[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2020121 RAB Revue Centre de documentation En réserve L003 Disponible A 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)PermalinkUnderstanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection / Chandi Witharana in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)PermalinkUnsupervised deep joint segmentation of multitemporal high-resolution images / Sudipan Saha in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkHigh-resolution remote sensing image scene classification via key filter bank based on convolutional neural network / Fengpeng Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkExploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution / Vitor Martins in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkBackground tropospheric delay in geosynchronous synthetic aperture radar / Dexin Li in Remote sensing, vol 12 n° 18 (September-2 2020)PermalinkHeliport detection using artificial neural networks / Emre Baseski in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)PermalinkAqueous alteration mapping in Rishabdev ultramafic complex using imaging spectroscopy / Hrishikesh Kumar in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)PermalinkA hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery / Mehdi Khoshboresh Masouleh in Applied geomatics, vol 12 n° 2 (June 2020)PermalinkObject-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery / Zhenhui Sun in Geocarto international, Vol 35 n° 8 ([01/06/2020])Permalink