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Impact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
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Titre : Impact of forest disturbance on InSAR surface displacement time series Type de document : Article/Communication Auteurs : Paula M. Bürgi, Auteur ; Rowena B. Lohman, Auteur Année de publication : 2021 Article en page(s) : pp 128 - 138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] changement d'occupation du sol
[Termes IGN] déboisement
[Termes IGN] détection de changement
[Termes IGN] détection du signal
[Termes IGN] erreur de phase
[Termes IGN] erreur systématique
[Termes IGN] image ALOS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] retard ionosphèrique
[Termes IGN] retard troposphérique
[Termes IGN] série temporelle
[Termes IGN] Sumatra
[Termes IGN] surveillance géologiqueRésumé : (auteur) As interferometric synthetic aperture radar (InSAR) data improve in their global coverage and temporal sampling, studies of ground deformation using InSAR are becoming feasible even in heavily vegetated regions such as the American Pacific Northwest (PNW) and Sumatra. However, ongoing forest disturbance due to logging, wildfires, or disease can introduce time-variable signals which could be misinterpreted as ground displacements. This study constrains the error introduced into InSAR time series in the presence of time-variable forest disturbance using synthetic data. For satellite platforms with randomly distributed orbital positions in time (e.g., Sentinel-1), mid-time series forest disturbance results in random error on the order of 0.2 and 10 cm/year for 1-year secular and time-variable velocities, respectively. If the orbital positions are not randomly distributed in time (e.g., ALOS-1), a biased error on the order of 10 cm/year is introduced to the inferred secular velocity. A time series using real ALOS-1 data near Eugene, OR, USA, shows agreement with the bias estimated by synthetic models. Mitigation of time-variable land cover change effects can be achieved if their timing is known, either through independent observations of surface properties (e.g., Landsat/Sentinel-2) or through the use of more computationally expensive, nonlinear inversions with additional terms for the timing of height changes. Inclusion of these additional terms reduces the potential for misinterpretation of InSAR signals associated with land surface change as ground deformation. Numéro de notice : A2021-032 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992938 Date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992938 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96727
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 128 - 138[article]Investigation of Sentinel-1 time series for sensitivity to fern vegetation in an European temperate forest / Marlin Mueller (2021)
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Titre : Investigation of Sentinel-1 time series for sensitivity to fern vegetation in an European temperate forest Type de document : Article/Communication Auteurs : Marlin Mueller, Auteur ; Clémence Dubois, Auteur ; Thomas Jagdhuber, Auteur ; Carsten Pathe, Auteur ; Christiane Schmullius, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2021 Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Allemagne
[Termes IGN] Filicophyta
[Termes IGN] forêt tempérée
[Termes IGN] image Sentinel-SAR
[Termes IGN] phénologie
[Termes IGN] série temporelle
[Termes IGN] variation saisonnièreMots-clés libres : Pteridium aquilinum Résumé : (auteur) In this study, a dense Copernicus Sentinel-1 time series is analyzed to gain a better understanding of the influence of undergrowth vegetation, in particular of eagle fern (Pteridium aquilinum), on the C-band SAR signal in a temperate forest in the Free State of Thuringia, Germany. Even if signals from the ground below the canopy may not be expected at C-band, previous studies showed seasonal fluctuations of the backscatter for temperate forests without canopy closure, notably for evergreen coniferous stands. Many factors can be responsible for these observed fluctuations, but in this study, we analyze one possible factor: the presence of undergrowth vegetation, in particular, of fern. Especially, the Sentinel-1 backscatter signal is analyzed for different acquisition configurations regarding its temporal and its spatial stability at different growth stages. This time series study shows that a difference of backscattered signal of up to 0.7 dB exists between forest patches with a dense fern density in the understory and the ones with low undergrowth vegetation. This signal difference depends on the season and is remarkably strong comparing winter (no fern undergrowth) with summer (major fern undergrowth). Numéro de notice : C2021-018 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Communication DOI : 10.5194/isprs-archives-XLIII-B3-2021-127-2021 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-127-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98070 Learning disentangled representations of satellite image time series in a weakly supervised manner / Eduardo Hugo Sanchez (2021)
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Titre : Learning disentangled representations of satellite image time series in a weakly supervised manner Type de document : Thèse/HDR Auteurs : Eduardo Hugo Sanchez, Auteur ; Mathieu Serrurier, Directeur de thèse ; Mathias Ortner, Directeur de thèse Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2021 Importance : 176 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Spécialité Informatique et TélécommunicationsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse des mélanges temporels
[Termes IGN] apprentissage automatique
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] image Sentinel-MSI
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation d'image
[Termes IGN] série temporelleIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This work focuses on learning data representations of satellite image time series via an unsupervised learning approach. The main goal is to enforce the data representation to capture the relevant information from the time series to perform other applications of satellite imagery. However, extracting information from satellite data involves many challenges since models need to deal with massive amounts of images provided by Earth observation satellites. Additionally, it is impossible for human operators to label such amount of images manually for each individual task (e.g. classification, segmentation, change detection, etc.). Therefore, we cannot use the supervised learning framework which achieves state-of-the-art results in many tasks.To address this problem, unsupervised learning algorithms have been proposed to learn the data structure instead of performing a specific task. Unsupervised learning is a powerful approach since no labels are required during training and the knowledge acquired can be transferred to other tasks enabling faster learning with few labels.In this work, we investigate the problem of learning disentangled representations of satellite image time series where a shared representation captures the spatial information across the images of the time series and an exclusive representation captures the temporal information which is specific to each image. We present the benefits of disentangling the spatio-temporal information of time series, e.g. the spatial information is useful to perform time-invariant image classification or segmentation while the knowledge about the temporal information is useful for change detection. To accomplish this, we analyze some of the most prevalent unsupervised learning models such as the variational autoencoder (VAE) and the generative adversarial networks (GANs) as well as the extensions of these models to perform representation disentanglement. Encouraged by the successful results achieved by generative and reconstructive models, we propose a novel framework to learn spatio-temporal representations of satellite data. We prove that the learned disentangled representations can be used to perform several computer vision tasks such as classification, segmentation, information retrieval and change detection outperforming other state-of-the-art models. Nevertheless, our experiments suggest that generative and reconstructive models present some drawbacks related to the dimensionality of the data representation, architecture complexity and the lack of disentanglement guarantees. In order to overcome these limitations, we explore a recent method based on mutual information estimation and maximization for representation learning without relying on image reconstruction or image generation. We propose a new model that extends the mutual information maximization principle to disentangle the representation domain into two parts. In addition to the experiments performed on satellite data, we show that our model is able to deal with different kinds of datasets outperforming the state-of-the-art methods based on GANs and VAEs. Furthermore, we show that our mutual information based model is less computationally demanding yet more effective. Finally, we show that our model is useful to create a data representation that only captures the class information between two images belonging to the same category. Disentangling the class or category of an image from other factors of variation provides a powerful tool to compute the similarity between pixels and perform image segmentation in a weakly-supervised manner. Note de contenu : Introduction
1- Background
2- Representation disentanglement via VAEs/GANs
3- Representation disentanglement via mutual information estimation
ConclusionNuméro de notice : 24065 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique et Télécommunications : Toulouse 3 : 2021 Organisme de stage : nstitut de Recherche en Informatique de Toulouse IRIT DOI : sans En ligne : http://thesesups.ups-tlse.fr/4971/1/2021TOU30032.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101822
Titre : Learning harmonised Pleiades and Sentinel-2 surface reflectances Type de document : Article/Communication Auteurs : J. Michel, Auteur ; Jordi Inglada, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2021 Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Importance : pp 265 - 272 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image Pléiades
[Termes IGN] image Sentinel-MSI
[Termes IGN] réflectance de surface
[Termes IGN] régression linéaireRésumé : (auteur) In this paper, we investigate the use of machine-learning techniques in order to produce harmonised surface reflectances between Sentinel-2 and Pleiades images, and reduce the impact of the differences in sensors, view conditions, and atmospheric correction differences between them. We demonstrate that if a simple linear regression considering Sentinel-2 surface reflectances as the target domain can overcome this problem when both images are calibrated to Top of Canopy reflectances, the non-linearity brought by a simple Multi-Layer-Perceptron is already useful when Pleiades is corrected to Top of Atmosphere level and contributions of the atmosphere need to be learned. We also demonstrate that learning a Convolution Neural Network instead of a plain MLP can learn undesired spatial effects such as mis-registration or differences in spatial frequency content, that will affect the image quality of the corrected Pleiades product. We overcome this issue by proposing an adhoc input convolutional layer that will capture those effects and can later be unplugged during inference. Last, we also propose an architecture and loss function that is robust to unmasked clouds and produces a confidence prediction during inference. Numéro de notice : C2021-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Communication DOI : 10.5194/isprs-archives-XLIII-B3-2021-265-2021 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-265-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98071
Titre : Mapping and Monitoring Forest Cover Type de document : Monographie Auteurs : Russell G. Congalton, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 144 p. ISBN/ISSN/EAN : 978-3-0365-2043-8 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte forestière
[Termes IGN] couvert forestier
[Termes IGN] forêt
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] inventaire forestier local
[Termes IGN] mangroveNote de contenu : - Mapping and Monitoring Forest Cover
- Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China
- Monitoring Mangrove Forest Degradation and Regeneration: Landsat Time Series Analysis of Moisture and Vegetation Indices at Rabigh Lagoon, Red Sea
- Monitoring Carbon Stock and Land-Use Change in 5000-Year-Old Juniper Forest Stand of Ziarat, Balochistan, through a Synergistic Approach
- Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data
- Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory
- A Comparison of Forest Tree Crown Delineation from Unmanned Aerial Imagery Using Canopy Height Models vs. Spectral LightnessNuméro de notice : 17141 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-2044-5 En ligne : https://doi.org/10.3390/books978-3-0365-2044-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98631 Mask R-CNN and OBIA fusion improves the segmentation of scattered vegetation in very high-resolution optical sensors / Emilio Guirado in Sensors, vol 21 n° 1 (January 2021)
PermalinkMonitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations / Shengbiao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
PermalinkNear-real-time identification of the drivers of deforestation in French Guiana / Marie Ballère (2021)
PermalinkA new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network / Wang Li in Advances in space research, vol 67 n° 1 (January 2021)
PermalinkPermalinkPermalinkProduction et mise à jour d’un produit BD Forêt V3 par apprentissage profond / Sébastien Giordano (2021)
PermalinkProposition d’un référentiel de description et de détection de la végétation dans une agglomération / Mathilde Segaud (2021)
PermalinkQualification des données LiDAR GEDI pour le suivi de l’impact climatique sur la forêt de Südharz / Iris Jeuffrard (2021)
PermalinkPermalinkPermalinkPermalinkReprésentation sémantique de données géospatiales au service de l'analyse de changements / Jordan Dorne (2021)
PermalinkA review of image fusion techniques for pan-sharpening of high-resolution satellite imagery / Farzaneh Dadrass Javan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
PermalinkSAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery / Marie Ballère in Remote sensing of environment, Vol 252 (January 2021)
PermalinkPermalinkSeasonal flow variability of Greenlandic glaciers : satellite observations and numerical modeling to study driving processes / Anna Derkacheva (2021)
PermalinkSemantic segmentation of sea ice type on Sentinel-1 SAR data using convolutional neural networks / Alissa Kouraeva (2021)
PermalinkPermalinkSpatio-temporal analysis of urbanization using GIS and remote sensing in developing countries / Yuji Murayama (2021)
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