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Global and climate challenges, graph-based data analysis for multisource information extraction / Morgane Batelier (2022)
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Titre : Global and climate challenges, graph-based data analysis for multisource information extraction Type de document : Mémoire Auteurs : Morgane Batelier, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 43 p. Format : 21 x 30 cm Note générale : Bibliographie
Mémoire de fin d'études, cycle des ingénieurs ENSG 3ème année, FRSLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Arctique, océan
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] glace de mer
[Termes IGN] image hyperspectrale
[Termes IGN] image Sentinel-SAR
[Termes IGN] polarimétrie radar
[Termes IGN] traitement d'image radarIndex. décimale : MPT Mémoires de fin d'études du Master Méthodes physiques en télédétection Résumé : (Auteur) During my end-of-studies internship, I worked on the development of a label propagation algorithm for remote sensing data, using Deep Learning. It was mainly applied to sea ice classification using SAR Sentinel-1 data, and to hyperspectral imaging in order to be effective to multimodal remote sensing. I started by the bibliography, during which we decided with my supervisors the method I was going to work from. Then, I worked on the algorithm implementation that was the longest phase. Finally, the last part of my work was the certification and improvement of the results using different process. Note de contenu : Introduction
1. Remote Sensing in the Arctic
1.1 Challenges of the Arctic
1.2 Sea Ice
2. Label Propagation for Deep Learning
2.1 Preliminaries
2.2 Transductive Propagation Network for Few-shot Learning
3. Multimodal Remote Sensing Data
3.1 Synthetic Aperture Radar
3.2 Hyperspectral Imaging
4. Experimental results
4.1 Datasets
4.2 Improvement Methods
4.3 Discussion and future of the algorithm
ConclusionNuméro de notice : 26935 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Mémoire de fin d'études IT Organisme de stage : Center for Integrated Remote Sensing and Forecasting for Arctic Operations CIRFA Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102059 Documents numériques
en open access
Global and climate challenges, graph-based data analysis for multisource information extraction - pdf auteurAdobe Acrobat PDFHigh-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach / Martin Schwartz (2022)
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Titre : High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach Type de document : Article/Communication Auteurs : Martin Schwartz, Auteur ; Philippe Ciais, Auteur ; Catherine Ottle, Auteur ; Aurélien de Truchis, Auteur ; Cédric Vega , Auteur ; Ibrahim Fayad, Auteur ; Martin Brandt, Auteur ; Rasmus Fensholt, Auteur ; Nicolas Baghdadi, Auteur ; François Morneau
, Auteur ; David Morin, Auteur ; Dominique Guyon, Auteur ; Sylvia Dayau, Auteur ; Jean-Pierre Wigneron, Auteur
Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] forêt
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] hauteur des arbres
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Landes de Gascogne
[Termes IGN] PinophytaRésumé : (auteur) In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km2 with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region. Numéro de notice : P2022-002 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2212.10265 Date de publication en ligne : 20/12/2022 En ligne : https://doi.org/10.48550/arXiv.2212.10265 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102850 Historical shoreline analysis and field monitoring at Ennore coastal stretch along the Southeast coast of India / M. Dhananjayan in Marine geodesy, vol 45 n° 1 (January 2022)
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Titre : Historical shoreline analysis and field monitoring at Ennore coastal stretch along the Southeast coast of India Type de document : Article/Communication Auteurs : M. Dhananjayan, Auteur ; S. Vasanthakumar, Auteur ; S.A. Sannasiraj, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 47 - 74 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] détection de changement
[Termes IGN] érosion côtière
[Termes IGN] image Landsat
[Termes IGN] Inde
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] régression linéaire
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (auteur) A shoreline change analysis has been carried out for the coastal stretch from Ennore creek to Karungali village located along the southeast coast of India. This 15 km-long coastal stretch had undergone significant changes such as erosion and accretion concerning infrastructure developments and leading to large impact on the livelihood of the community. To assess the shoreline changes, the analysis of multi-temporal satellite images has been carried out. A historical trend is established for the study period from 1991 to 2019. The analysis has been made in three timelines considering various developing activities. There was no significant coastal infrastructure development during 1991 to 1999; however, between 1999 and 2009, a major port, pier, and a groyne field were constructed. Additionally, a port was established between 2009 and 2019. Erosion was observed on the coast from Kattupalli to Karungali at a rate of −16.85 m/yr since 2009, while the coast on the south of Ennore port is accreting at the rate of +12.43 m/yr during the same period. The near-future projection using a linear regression model shows further erosion in the coast under similar conditions. The results of this study provide a baseline data for future anthropogenic activities along this coast. Numéro de notice : A2022-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01490419.2021.1992546 Date de publication en ligne : 08/11/2021 En ligne : https://doi.org/10.1080/01490419.2021.1992546 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99370
in Marine geodesy > vol 45 n° 1 (January 2022) . - pp 47 - 74[article]Implementation of the log-transformed band ratio algorithm on images of WorldView-3 and Sentinel-2 for bathymetry mapping of a pocket beach of Malta / Antoine Cornu (2022)
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Titre : Implementation of the log-transformed band ratio algorithm on images of WorldView-3 and Sentinel-2 for bathymetry mapping of a pocket beach of Malta Type de document : Article/Communication Auteurs : Antoine Cornu, Auteur ; Luciano Galone, Auteur ; Arnaud Le Bris , Auteur ; Sebastiano d' Amico, Auteur ; Adam Gauci, Auteur ; Manchun Lei
, Auteur ; Emanuele Colica, Auteur
Editeur : New-York : IEEE Computer society Année de publication : 2022 Conférence : MetroSea 2022, IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters 03/10/2022 05/10/2022 Milazzo Italie Importance : pp 493 - 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bathymétrie
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Worldview
[Termes IGN] littoral
[Termes IGN] profondeurMots-clés libres : log-transformed band ratio depth retrieval Résumé : (auteur) Several methods are in place to calculate shallow water bathymetry from satellite images, such as Worldview 3 or Sentinel-2, which have differences in their resolution and their accessibility. The method used in this research is the log- transformed band ratio between the blue channel and the green channel. This document compares the results of this method with the Worldview 3 and Sentinel-2 images. Numéro de notice : C2022-045 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/MetroSea55331.2022.9950982 En ligne : https://doi.org/10.1109/MetroSea55331.2022.9950982 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102290 Improving LSMA for impervious surface estimation in an urban area / Jin Wang in European journal of remote sensing, vol 55 n° 1 (2022)
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Titre : Improving LSMA for impervious surface estimation in an urban area Type de document : Article/Communication Auteurs : Jin Wang, Auteur ; Yaolong Zhao, Auteur ; Yingchun Fu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 37 - 51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification et arbre de régression
[Termes IGN] image Landsat-OLI
[Termes IGN] régression
[Termes IGN] signature spectrale
[Termes IGN] surface imperméable
[Termes IGN] Yunnan (Chine)
[Termes IGN] zone urbaineRésumé : (auteur) Linear spectral mixture analysis (LSMA) and regression analysis are the two most conventionally used methods to estimate impervious surfaces at the subpixel scale in an urban area. However, LSMA lacks the sensitivity to pixel brightness, which leads to inter variability of endmembers and affects the ability to distinguish features with a similar spectral signature. This research aims to develop LSMA aided by a regression analysis model to estimate impervious surfaces with higher accuracy. A spectral angle mapping (SAM) based regression analysis model is introduced to reduce errors. Based on high-resolution images and field survey data, the SAM-based regression analysis can estimate non-impervious surface and high-impervious surface densities with high accuracy, while less accurate in impervious surfaces with low/medium density. In contrast, LSMA is able to estimate low/medium-density impervious surfaces with higher accuracy. We propose an improved approach by integrating the two methods, regression analysis aided LSMA, for impervious surface estimation. The proposed method increases the overall accuracy of the impervious surface estimation to 85.24%, which is significantly greater than that of the conventional methods. Numéro de notice : A2022-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1080/22797254.2021.2018666 Date de publication en ligne : 05/01/2022 En ligne : https://doi.org/10.1080/22797254.2021.2018666 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99548
in European journal of remote sensing > vol 55 n° 1 (2022) . - pp 37 - 51[article] PermalinkInvestigating the role of wind disturbance in tropical forests through a forest dynamics model and satellite observations / E-Ping Rau (2022)
PermalinkLatent heat flux variability and response to drought stress of black poplar: A multi-platform multi-sensor remote and proximal sensing approach to relieve the data scarcity bottleneck / Flavia Tauro in Remote sensing of environment, vol 268 (January 2022)
PermalinkLearning spatio-temporal representations of satellite time series for large-scale crop mapping / Vivien Sainte Fare Garnot (2022)
PermalinkMapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine / Jiyu Liu in Geomatics, Natural Hazards and Risk, vol 13 (2022)
PermalinkModélisations des écoulements fluviaux adaptées aux observations spatiales et assimilations de données altimétriques / Thibault Malou (2022)
PermalinkMonitoring and analysis of crop irrigation dynamics in Central Italy through the use of MODIS NDVI data / Marta Chiesi in European journal of remote sensing, vol 55 n° 1 (2022)
PermalinkMonitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)
PermalinkMonitoring grassland dynamics by exploiting multi-modal satellite image time series / Anatol Garioud (2022)
PermalinkPreparation of the VENµS satellite data over Israel for the input into the GRASP data treatment algorithm / Maeve Blarel (2022)
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