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Termes IGN > imagerie
imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening / Hao Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
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Titre : GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening Type de document : Article/Communication Auteurs : Hao Zhang, Auteur ; Jiayi Ma, Auteur Année de publication : 2021 Article en page(s) : pp 223 - 239 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] fusion d'images
[Termes IGN] gradient
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] régressionRésumé : (auteur) Pansharpening aims to fuse low-resolution multi-spectral image and high-resolution panchromatic (PAN) image to produce a high-resolution multi-spectral (HRMS) image. In this paper, a new residual learning network based on gradient transformation prior, termed as GTP-PNet, is proposed to generate the high-quality HRMS image with accurate spectral distribution as well as reasonable spatial structure. Different from previous deep models that only rely on supervision of the HRMS reference image, we introduce the gradient transformation prior to the deep model, so as to improve the solution accuracy. Our model consists of two networks, namely gradient transformation network (TNet) and pansharpening network (PNet). TNet is committed to seeking the nonlinear mapping between gradients of PAN and HRMS images, which is essentially a spatial relationship regression of imaging bands in different ranges. PNet is the residual learning network used to generate the HRMS image, which is not only supervised by the HRMS reference image, but also constrained by the trained TNet. As a result, the HRMS image generated by PNet not only approximates the HRMS reference image in the spectral distribution, but also conforms to the gradient transformation prior in the spatial structure. Experimental results demonstrate the significant superiority of our method over the current state-of-the-arts in terms of both subjective visual effect and quantitative metrics. We also apply our method to produce the HR normalized difference vegetation index in remote sensing, which can achieve the best performance. Moreover, our method is much competitive compared with the state-of-the-art alternatives in running efficiency. Numéro de notice : A2021-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.12.014 Date de publication en ligne : 11/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.12.014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96859
in ISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 223 - 239[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 081-2021021 SL Revue Centre de documentation Revues en salle Disponible 081-2021022 DEP-RECF Revue Nancy Bibliothèque Nancy IFN Exclu du prêt Influence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle / Lucas Santos Santana in European journal of remote sensing, vol 54 n° 1 (2021)
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Titre : Influence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle Type de document : Article/Communication Auteurs : Lucas Santos Santana, Auteur ; Gabriel Araújo E Silva Ferraz, Auteur ; Diego Bedin Marin, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 59 - 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] altitude
[Termes IGN] capteur aérien
[Termes IGN] géoréférencement
[Termes IGN] hauteur de vol
[Termes IGN] image captée par drone
[Termes IGN] Minas Gerais (Brésil)
[Termes IGN] modèle numérique de terrain
[Termes IGN] photogrammétrie aérienne
[Termes IGN] point d'appui
[Termes IGN] précision géométrique (imagerie)Résumé : (auteur) This study aimed to explore the influence of flight altitude, density, and distribution of ground control points (GCPs) on the digital terrain model (DTM) in surveys conducted by unmanned aerial vehicles (UAVs). A total of 144 photogrammetric projects consisting of 399 aerial photos were carried out in a 2 ha area. These photogrammetric projects involved six GCP distributions (edge, center, diagonal, parallel, stratified, and random), six GCP densities, and four flight altitudes (30, 60, 90, and 120 m). The response surface methodology was used to find interference factors and total root-mean-square error (RMSEt) as well. The 60 m flight altitude presented was the most efficient. Central GCP distribution was observed to have low precision. Using stratified and random edge distributions, 10 GCPs are recommended to achieve geometric precision below 0.07 m at any flight height. However, for studies requiring up to 0.07 m precision, the best distribution was parallel with 4 GCPs at any altitude. Diagonal positioning of the GCPs showed RMSEt values below 0.11 m with 4 GCPs at any altitude. A good distribution of GCPs was found to be important, but the density of GCPs per image was more relevant when obtaining a lower RMSEt. Numéro de notice : A2021-155 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1845104 Date de publication en ligne : 10/01/2021 En ligne : https://doi.org/10.1080/22797254.2020.1845104 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97018
in European journal of remote sensing > vol 54 n° 1 (2021) . - pp 59 - 71[article]Monitoring the spatiotemporal dynamics of urban green space and Its impacts on thermal environment in Shenzhen city from 1978 to 2018 with remote sensing data / Yue Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)
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Titre : Monitoring the spatiotemporal dynamics of urban green space and Its impacts on thermal environment in Shenzhen city from 1978 to 2018 with remote sensing data Type de document : Article/Communication Auteurs : Yue Liu, Auteur ; Hui Li, Auteur ; Peng Gao, Auteur ; Cheng Zhong, Auteur Année de publication : 2021 Article en page(s) : pp 81 - 89 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] croissance urbaine
[Termes IGN] données spatiotemporelles
[Termes IGN] dynamique spatiale
[Termes IGN] espace vert
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat
[Termes IGN] impact sur l'environnement
[Termes IGN] Shenzhen
[Termes IGN] urbanismeRésumé : (Auteur) In a developing city, urban green space (UGS) plays an increasingly significant role in improving the urban environment and beautifying the urban landscape. In the meantime, UGS has been substantially and frequently interfered with by human activities. Taking Shenzhen city (a great metropolis of China) as an example, this study investigated the spatio-temporal dynamics of UGS and its influence on the urban thermal environment with Landsat images. From 1978 to 2018, all croplands and more than 50% of water bodies disappeared, while the built-up area increased more than 6 times. The rapid expansion of impervious surface and loss of UGS led to the spread of a surface urban heat island. The study shows that UGS has a significantly mitigating impact on urban land surface temperature, with cold islands mainly located at city parks. The results will be of great significance for improving UGS management, alleviating the urban heat island effect, and establishing a sustainable eco-environment. Numéro de notice : A2021-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.2.81 Date de publication en ligne : 01/02/2021 En ligne : https://doi.org/10.14358/PERS.87.2.81 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97040
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 2 (February 2021) . - pp 81 - 89[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021021 SL Revue Centre de documentation Revues en salle Disponible Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification / Zitong Wu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
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Titre : Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification Type de document : Article/Communication Auteurs : Zitong Wu, Auteur ; Biao Hou, Auteur ; Licheng Jiao, Auteur Année de publication : 2021 Article en page(s) : pp 1200 - 1213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification contextuelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image radar moiréeRésumé : (auteur) Synthetic aperture radar (SAR) image classification is a fundamental research direction in image interpretation. With the development of various intelligent technologies, deep learning techniques are gradually being applied to SAR image classification. In this study, a new SAR classification algorithm known as the multiscale convolutional neural network with an autoencoder regularization joint contextual attention network (MCAR-CAN) is proposed. The MCAR-CAN has two branches: the autoencoder regularization branch and the context attention branch. First, autoencoder regularization is used for the reconstruction of the input to regularize the classification in the autoencoder regularization branch. Multiscale input and an asymmetric structure of the autoencoder branch cause the network more to be focused on classification than on reconstruction. Second, the attention mechanism is used to produce an attention map in which each attention weight corresponds to a context correlation in attention branch. The robust features are obtained by the attention mechanism. Finally, the features obtained by the two branches are spliced for classification. In addition, a new training strategy and a postprocessing method are designed to further improve the classification accuracy. Experiments performed on the data from three SAR images demonstrated the effectiveness and robustness of the proposed algorithm. Numéro de notice : A2021-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3004911 Date de publication en ligne : 07/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3004911 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96918
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1200 - 1213[article]Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam / Vu Anh Tuan in European journal of remote sensing, vol 54 n° 1 (2021)
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Titre : Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam Type de document : Article/Communication Auteurs : Vu Anh Tuan, Auteur ; Nguyen Hong Quang, Auteur ; le Thi Thu Hang, Auteur Année de publication : 2021 Article en page(s) : pp 13 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande L
[Termes IGN] cartographie des risques
[Termes IGN] crue
[Termes IGN] image ALOS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] Mekong (fleuve)
[Termes IGN] optimisation spatiale
[Termes IGN] surveillance hydrologique
[Termes IGN] Viet NamRésumé : (auteur) One major characteristic of floods is flood extent. Information on this characteristic is indispensable for flood monitoring. Recently, synthetic aperture radar (SAR) data have been increasing in quality and quantity. This allows more flood studies conducted over large areas regardless of cloud and weather conditions and provides advantages including clear surface water classification based on SAR scattering mechanisms for low values (open water) and high values (inundated vegetation, etc.). However, challenges remain due to sources of uncertainties, such as atmospheric disturbances and vegetation masking parts of water surfaces. Therefore, in this study, we aim to optimize flood mapping processes on flooded vegetation that generated high-value pixels based on a SAR scattering mechanism called double bounce that classifies vegetative flooded water in L-band SAR images. This optimization is nearly impossible using Sentinel-1 scenes. Backscattering of time-series Sentinel-1 and ALOS-2 images acquired for the 2018 and 2019 flood season was analysed, thresholded and hybridized for flood mapping of a study site in the Tam Nong district of the Dong Thap Province of Vietnam. We found that the accuracy of SAR flood maps was improved compared to ground truth data when the SAR-extracted vegetative-flooded plains were considered flooded. Numéro de notice : A2021-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1859340 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1859340 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97015
in European journal of remote sensing > vol 54 n° 1 (2021) . - pp 13 - 28[article]Reclaimed-airport surface-deformation monitoring by improved permanent-scatterer interferometric synthetic-aperture radar: a case study of Shenzhen Bao'an international airport, China / Lu Miao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)
PermalinkSAR image speckle reduction based on nonconvex hybrid total variation model / Yuli Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
PermalinkSemi-supervised joint learning for hand gesture recognition from a single color image / Chi Xu in Sensors, vol 21 n° 3 (February 2021)
PermalinkSpruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery / Rajeev Bhattarai in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
PermalinkStudy of systematic bias in measuring surface deformation with SAR interferometry / Homa Ansari in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
PermalinkTropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
PermalinkMapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)
PermalinkUsing Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas / José P. Granadeiro in Remote sensing, Vol 13 n° 2 (January-2 2021)
Permalink30èmes Journées de la Recherche de l'IGN - ENSG dématérialisées du 25 au 28 mai 2021 / Journées Recherche de l'IGN 2021, 30èmes journées (25 - 28 mai 2021; France) (2021)
PermalinkPermalink3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)
PermalinkAccurate sea surface heights from Sentinel-3A and Jason-3 retrackers by incorporating high-resolution marine geoid and hydrodynamic models / Mir Abolfazl Mostafavi in Journal of geodetic science, vol 11 n° 1 (January 2021)
PermalinkAleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis / Max Mehltretter in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
PermalinkAmélioration des résolutions spatiale et spectrale d’images satellitaires par réseaux antagonistes / Anaïs Gastineau (2021)
PermalinkAmélioration des systèmes de suivi des cultures à l’aide de la télédétection multi-source et des techniques d’apprentissage profond / Yawogan Gbodjo (2021)
PermalinkAn improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images / Behrooz Moradi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)
PermalinkAnalyse de la dynamique d’embroussaillement des pelouses calcaires par traitement d’images / Théo Mesure (2021)
PermalinkAnalyse spatio-temporaire des dégradations et évolution des forêts par télédétection : cas du Parc National de Theniet El Had (Algérie) / Faouzi Berrichi in Bulletin des sciences géographiques, n° 32 (2019 - 2021)
PermalinkApplications of remote sensing data in mapping of forest growing stock and biomass / Jose Aranha (2021)
PermalinkApport des données satellitaires Sentinel-1 et Sentinel-2 pour la détection des surfaces irriguées et l'estimation des besoins et des consommations en eau des cultures d'été dans les zones tempérées / Yann Pageot (2021)
PermalinkApport des données Sentinel-1 pour le suivi continu de la forêt tropicale : Cas de la Guyane / Marie Ballère (2021)
PermalinkApport des méthodes : imagerie drone, LiDAR et imagerie hyperspectrale pour l’étude du littoral vendéen / Mathis Baudis (2021)
PermalinkApport de la modélisation physique pour la cartographie de la biodiversité végétale en forêts tropicales par télédétection optique / Dav Ebengo Mwampongo (2021)
PermalinkApport de la photogrammétrie satellite pour la modélisation du manteau neigeux / César Deschamps-Berger (2021)
PermalinkApport de la télédétection pour la simulation spatialisée des composantes du bilan carbone des cultures et des effets d'atténuation biogéochimiques et biogéophysiques des cultures intermédiaires / Gaétan Pique (2021)
PermalinkApports des méthodes d'apprentissage profond pour la reconnaissance automatique des modes d'occupation des sols et d'objets par télédétection en milieu tropical / Guillaume Rousset (2021)
PermalinkPermalinkPermalinkPermalinkAssessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin / Luis Felipe Galizia in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)
PermalinkAssessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)
PermalinkAssessment of chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data / Ioannis Moutzouris-Sidiris in Open geosciences, vol 13 n° 1 (January 2021)
PermalinkAssessment of combining convolutional neural networks and object based image analysis to land cover classification using Sentinel 2 satellite imagery (Tenes region, Algeria) / N. Zaabar (2021)
PermalinkPermalinkAssessment of sky diffuse irradiance and building reflected irradiance in cast shadows / Manchun Lei (2021)
PermalinkAutomated detection of individual Juniper tree location and forest cover changes using Google Earth Engine / Sudeera Wickramarathna in Annals of forest research, vol 64 n° 1 (2021)
PermalinkAutomatic object extraction from airborne laser scanning point clouds for digital base map production / Elyta Widyaningrum (2021)
PermalinkBeach morphology and its dynamism from remote sensing for coastal management support / Carlos Cabezas Rabadán (2021)
PermalinkBenchmarking of convolutional neural network approaches for vegetation land cover mapping / Benjamin Carpentier (2021)
PermalinkCartographie dense et compacte par vision RGB-D pour la navigation d’un robot mobile / Bruce Canovas (2021)
PermalinkCentrality and city size effects on NO2 ground and tropospheric concentrations within European cities / Yufei Wei (2021)
PermalinkChange detection of land use and land cover, using landsat-8 and sentinel-2A images / Mohammed Abdulmohsen Alhedyan (2021)
PermalinkCharacterization of mass variations in Antarctica in response to climatic fluctuations from space-based gravimetry and radar altimetry data / Athul Kaitheri (2021)
PermalinkPermalinkCluttering reduction for interactive navigation and visualization of historical Images / Evelyn Paiz-Reyes (2021)
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