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Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms / Yadong Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 3000224 Titre : Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms Type de document : Article/Communication Auteurs : Yadong Li, Auteur ; Sébastien Mavromatis, Auteur ; Feng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 3000224 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] classification par réseau neuronal convolutif [Termes IGN] image isolée [Termes IGN] pouvoir de résolution géométrique [Termes IGN] pouvoir de résolution spectrale [Termes IGN] reconstruction d'image [Termes IGN] réseau antagoniste génératif Résumé : (auteur) Super-resolution (SR) technology is an important way to improve spatial resolution under the condition of sensor hardware limitations. With the development of deep learning (DL), some DL-based SR models have achieved state-of-the-art performance, especially the convolutional neural network (CNN). However, considering that remote sensing images usually contain a variety of ground scenes and objects with different scales, orientations, and spectral characteristics, previous works usually treat important and unnecessary features equally or only apply different weights in the local receptive field, which ignores long-range dependencies; it is still a challenging task to exploit features on different levels and reconstruct images with realistic details. To address these problems, an attention-based generative adversarial network (SRAGAN) is proposed in this article, which applies both local and global attention mechanisms. Specifically, we apply local attention in the SR model to focus on structural components of the earth’s surface that require more attention, and global attention is used to capture long-range interdependencies in the channel and spatial dimensions to further refine details. To optimize the adversarial learning process, we also use local and global attentions in the discriminator model to enhance the discriminative ability and apply the gradient penalty in the form of hinge loss and loss function that combines L1 pixel loss, L1 perceptual loss, and relativistic adversarial loss to promote rich details. The experiments show that SRAGAN can achieve performance improvements and reconstruct better details compared with current state-of-the-art SR methods. A series of ablation investigations and model analyses validate the efficiency and effectiveness of our method. Numéro de notice : A2022-767 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3093043 Date de publication en ligne : 12/07/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3093043 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101789 [article]

Understanding of atmospheric systems with efficient numerical methods for observation and prediction / Lei-Ming Ma (2019)  

Public Titre : Understanding of atmospheric systems with efficient numerical methods for observation and prediction Type de document : Monographie Auteurs : Lei-Ming Ma, Éditeur scientifique ; Feng Zhang, Éditeur scientifique ; Chang-Jiang Zhang, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2019 Importance : 168 p. ISBN/ISSN/EAN : 978-1-83880-634-7 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Chine [Termes IGN] cyclone [Termes IGN] image infrarouge [Termes IGN] image radar [Termes IGN] image satellite [Termes IGN] Madrid (Espagne) [Termes IGN] observation de la Terre [Termes IGN] phénomène atmosphérique [Termes IGN] pluie [Termes IGN] polarisation [Termes IGN] prévision météorologique [Termes IGN] qualité de l'air [Termes IGN] température de l'air [Termes IGN] zone urbaine Résumé : (Editeur) Although the technology of observation and prediction of atmospheric systems draws upon many common fields, until now the interrelatedness and interdisciplinary nature of these research fields have scarcely been discussed in one volume containing fundamental theories, numerical methods, and operational application results. This is a book to provide in-depth explorations of the numerical methods developed to better understand atmospheric systems, which are introduced in eight chapters. Chapter 1 presents an efficient algorithm for tropical cyclone center determination by using satellite imagery. Chapter 2 aims to identify atmospheric systems with a new polarization remote sensing method. Chapters 3-8 place emphasis on enhancing the performance of numerical models in the prediction of atmospheric systems that should be valuable for researchers and forecasters. Note de contenu : 1. Introductory Chapter: Understanding of Atmospheric Systems with Efficient Numerical Methods for Observation and Prediction / Lei-Ming Ma 2. Tropical Cyclone Center Determination Algorithm by Texture and Gradient of Infrared Satellite Image / Chang-Jiang Zhang, Qi Luo, Yuan Chen, Juan Lu, Li-Cheng Xue and Xiao-Qin Lu 3. Polarization Remote Sensing for Land Observation / Lei Yan, Taixia Wu and Xueqi Wang 4. Rainfall Nowcasting by Blending of Radar Data and Numerical Weather Prediction / Hai Chu, Mengjuan Liu, Min Sun and Lei Chen 5. Spectral Representation of Time and Physical Parameters in Numerical Weather Prediction / Kristoffer Lindvall and Jan Scheffel 6. Atmospheric Radiative Transfer Parameterizations / Feng Zhang, Yi-Ning Shi, Kun Wu, Jiangnan Li and Wenwen Li 7. Evaluating Cooling Tower Scheme and Mechanical Drag Coefficient Formulation in High-Resolution Regional Model / Miao Yu and Shiguang Miao 8. Numerical Air Quality Forecast over Eastern China: Development, Uncertainty and Future / Guangqiang Zhou, Zhongqi Yu, Yixuan Gu and Luyu Chang 9. Numerical Simulation of the Effects of Increasing Urban Albedo on Air Temperatures and Quality over Madrid City (Spain) by Coupled WRF/CMAQ Atmospheric Chemistry Model / Pablo Campra Numéro de notice : 26506 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.76493 En ligne : https://doi.org/10.5772/intechopen.76493 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97085

vol 35 n°5 - September 2011 - Sustainable Urban Development (Bulletin de Computers, Environment and Urban Systems) / Feng Zhang  

Public [n° ou bulletin]  Titre : vol 35 n°5 - September 2011 - Sustainable Urban Development Type de document : Périodique Auteurs : Feng Zhang, Auteur ; Anthony G.O. Yeh, Auteur Année de publication : 2011 Langues : Anglais (eng) Numéro de notice : 239-201105 Affiliation des auteurs : non IGN Nature : Numéro de périodique En ligne : http://www.sciencedirect.com/science/journal/01989715/35/5 Format de la ressource électronique : URL Sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=26478 [n° ou bulletin]