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Super-resolution of VIIRS-measured ocean color products using deep convolutional neural network / Xiaoming Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
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[article]
Titre : Super-resolution of VIIRS-measured ocean color products using deep convolutional neural network Type de document : Article/Communication Auteurs : Xiaoming Liu, Auteur ; Menghua Wang, Auteur Année de publication : 2021 Article en page(s) : pp 114 - 127 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] apprentissage profond
[Termes IGN] bande infrarouge
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couleur de l'océan
[Termes IGN] image infrarouge couleur
[Termes IGN] image multibande
[Termes IGN] image NPP-VIIRS
[Termes IGN] rayonnementRésumé : (auteur) Since its launch in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has provided high quality global ocean color products, which include normalized water-leaving radiance spectra nLw ( λ ) of six moderate (M) bands (M1–M6) at the wavelengths of 410, 443, 486, 551, 671, and 745 nm with a spatial resolution of 750-m, and one imagery (I) band at a wavelength of 638 nm with a spatial resolution of 375-m. Because the high-resolution I-band measurements are highly correlated spectrally to those of M-band data, it can be used as a guidance to super-resolve the M-band nLw ( λ ) imagery from 750- to 375-m spatial resolution. Super-resolving images from coarse spatial resolution to finer ones have been a field of very active research in recent years. However, no previous studies have been applied to satellite ocean color remote sensing, in particular, for VIIRS ocean color applications. In this study, we employ the deep convolutional neural network (CNN) technique to glean the high-frequency content from the VIIRS I1 band and transfer to super-resolved M-band ocean color images. The network is trained to super-resolve each of the VIIRS six M-bands nLw ( λ ) separately. In our results, the super-resolved (375-m) nLw ( λ ) images are much sharper and show finer spatial structures than the original images. Quantitative evaluations show that biases between the super-resolved and original nLw ( λ ) images are small for all bands. However, errors in the super-resolved nLw ( λ ) images are wavelength-dependent. The smallest error is found in the super-resolved nLw (551) and nLw (671) images, and error increases as the wavelength decreases from 486 to 410 nm. The results show that the networks have the capability to capture the correlations of the M-band and the I1 band images to super-resolved M-band images. Numéro de notice : A2021-031 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992912 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992912 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96726
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 114 - 127[article]Supplementary material for: Panoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)
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Titre : Supplementary material for: Panoptic segmentation of satellite image time series with convolutional temporal attention networks Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu
, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Conférence : ICCV 2021, IEEE/CVF International Conference on Computer Vision 11/10/2021 17/10/2021 programme Importance : pp 1 - 8 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] contour
[Termes IGN] Pastis
[Termes IGN] Perceptron multicouche
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelleRésumé : (auteur) In this appendix, we provide additional information on the PASTIS dataset and our exact model configuration. We also provide complementary qualitative experimental results. Numéro de notice : C2021-024 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98728 Voir aussiDocuments numériques
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Supplementary material for: Panoptic... - pdf auteur-Adobe Acrobat PDF
Titre : Système expert et télédétection pour la cartographie des habitats : Apports et valorisations pour la conservation de la biodiversité de l’estuaire de la Loire Type de document : Thèse/HDR Auteurs : Mathieu Le Dez, Auteur ; Marc Robin, Directeur de thèse ; Patrick Launeau, Directeur de thèse Editeur : Nantes : Université de Nantes Année de publication : 2021 Importance : 350 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université de Nantes, spécialité GéographieLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte thématique
[Termes IGN] conservation des ressources naturelles
[Termes IGN] données lidar
[Termes IGN] estuaire
[Termes IGN] habitat d'espèce
[Termes IGN] image Sentinel-MSI
[Termes IGN] inventaire
[Termes IGN] Loire (bassin)
[Termes IGN] modèle numérique de terrain
[Termes IGN] relevé phytosociologique
[Termes IGN] site Natura 2000
[Termes IGN] système expertIndex. décimale : THESE Thèses et HDR Résumé : (auteur) L’inventaire et la cartographie des habitats sont des processus essentiels pour la mise en oeuvre des politiques de conservation de la nature. Les méthodes actuelles, basées sur des prospections de terrain, sont difficilement applicables sur de vastes territoires et jugées inadaptées à un suivi régulier et harmonisé des habitats. L’objectif de cette thèse est d’explorer des approches innovantes afin de faciliter l’inventaire et la cartographie des habitats sur de grands sites naturels, en prenant comme cas d’étude le site Natura 2000 ‘Estuaire de la Loire’. Un système expert a été développé pour l’identification de relevés phytosociologiques afin d’établir la typologie des habitats du site. Cette démarche a permis de rattacher de manière formelle 1843 relevés de végétation à 89 habitats EUNIS et 17 habitats d’intérêt communautaire. Des images satellites Sentinel-2 et des données aéroportées hyperspectrales et LiDAR ont été exploitées pour spatialiser les habitats du site par télédétection. Ces différentes données, aux caractéristiques complémentaires (résolutions spatiales, résolutions spectrales, répétitivité, 3D), ont permis de cartographier avec une très grande précision la majorité des habitats des 24 000 ha de l’estuaire de la Loire. L’application de ces nouvelles approches démontre l’intérêt d’associer les systèmes experts et la télédétection pour typifier et cartographier des habitats de façon rentable et reproductible favorisant une gestion concertée du site Natura 2000. Note de contenu : Introduction générale
1- Elaboration de la typologie des habitats de l’estuaire de la Loire
2- Cartographie des habitats de l’estuaire de la Loire par télédétection
3- Analyse des usages de la cartographie des végétations et des habitats par les praticiens
Synthèse et conclusion généraleNuméro de notice : 28665 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Géographie : Nantes : 2021 Organisme de stage : LETG Littoral DOI : sans En ligne : https://archive.bu.univ-nantes.fr/pollux/show/show?id=eb664035-71eb-4900-a09d-7c [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99861 The challenge of robust trait estimates with deep learning on high resolution RGB images / Etienne David (2021)
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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 The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. Rukhovitch in Remote sensing, vol 13 n° 1 (January-1 2021)
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Titre : The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution Type de document : Article/Communication Auteurs : Dimitri I. Rukhovitch, Auteur ; Polina V. Koroleva, Auteur ; Danila D. Rukhovitch, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dégradation des sols
[Termes IGN] distribution spatiale
[Termes IGN] érosion
[Termes IGN] image Landsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Russie
[Termes IGN] surface cultivée
[Termes IGN] système d'information géographiqueRésumé : (auteur) Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps. Numéro de notice : A2021-074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010155 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/rs13010155 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96810
in Remote sensing > vol 13 n° 1 (January-1 2021) . - n° 155[article] PermalinkUnifying remote sensing image retrieval and classification with robust fine-tuning / Dimitri Gominski (2021)
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PermalinkVegetation stratum occupancy prediction from airborne LiDAR 3D point clouds / Ekaterina Kalinicheva (2021)
PermalinkVisual exploration of historical image collections: An interactive approach through space and time / Evelyn Paiz-Reyes (2021)
PermalinkAutomatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)
PermalinkCartographic generalization / Monika Sester in Journal of Spatial Information Science, JoSIS, n° 21 (2020)
PermalinkA deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer / Xing Yan in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
PermalinkDeep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination / Frederik Hass in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)
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