Descripteur
Documents disponibles dans cette catégorie (108)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Extraction of impervious surface using Sentinel-1A time-series coherence images with the aid of a Sentinel-2A image / Wenfu Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)
[article]
Titre : Extraction of impervious surface using Sentinel-1A time-series coherence images with the aid of a Sentinel-2A image Type de document : Article/Communication Auteurs : Wenfu Wu, Auteur ; Jiahua Teng, Auteur ; Qimin Cheng, Auteur ; Songjing Guo, Auteur Année de publication : 2021 Article en page(s) : pp 161-170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] chatoiement
[Termes IGN] cohérence (physique)
[Termes IGN] cohérence temporelle
[Termes IGN] extraction automatique
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] segmentation d'image
[Termes IGN] segmentation multi-échelle
[Termes IGN] série temporelle
[Termes IGN] surface imperméableRésumé : (Auteur) The continuous increasing of impervious surface (IS) hinders the sustainable development of cities. Using optical images alone to extract IS is usually limited by weather, which obliges us to develop new data sources. The obvious differences between natural and artificial targets in interferometric synthetic-aperture radar coherence images have attracted the attention of researchers. A few studies have attempted to use coherence images to extract IS—mostly single-temporal coherence images, which are affected by de-coherence factors. And due to speckle, the results are rather fragmented. In this study, we used time-series coherence images and introduced multi-resolution segmentation as a postprocessing step to extract IS. From our experiments, the results from the proposed method were more complete and achieved considerable accuracy, confirming the potential of time-series coherence images for extracting IS. Numéro de notice : A2021-240 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.3.161 Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.14358/PERS.87.3.161 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97264
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 3 (March 2021) . - pp 161-170[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021031 SL Revue Centre de documentation Revues en salle Disponible Assessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)
Titre : Assessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Clément Mallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IGARSS 2021, IEEE International Geoscience And Remote Sensing Symposium 11/07/2021 16/07/2021 Bruxelles Belgique Proceedings IEEE Importance : pp 3105 - 3108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] détection de changement
[Termes IGN] image Sentinel-SAR
[Termes IGN] parcelle agricole
[Termes IGN] prairie
[Termes IGN] régression multiple
[Termes IGN] série temporelleRésumé : (auteur) One key factor to exhaustive vegetation monitoring lies in the dense temporal sampling of the measurements. Areas subject to multiple human interventions, such as grasslands, are particularly concerned. A Recurrent Neural Network multi-sensor regression approach (SenRVM), relying on the systematic acquisitions of Sentinel-1 SAR satellite, has been thereby proposed. It permits to retrieve vegetation indexes, derived from Sentinel-2 optical imagery, despite significant cloud cover and with high sampling (6 days). The benefit of SenRVM for filling gaps in vegetation time-series describing agricultural practices is assessed. The proposed approach is compared with classical mono-sensor optical strategies. We adopt a synthetic dataset with large gaps. This realistically mimicks challenging conditions in grassland exploitation detection. Results obtained both for exploited and stable parcels satisfactorily demonstrate the relevance of our approach. Numéro de notice : C2021-042 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS47720.2021.9554995 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.1109/IGARSS47720.2021.9554995 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99413 Deep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)
Titre : Deep learning for wildfire progression monitoring using SAR and optical satellite image time series Type de document : Thèse/HDR Auteurs : Puzhao Zhang, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 2021 Importance : 100 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-91-7873-935-6 Note générale : bibliographie
Doctoral Thesis in GeoinformaticsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Alberta (Canada)
[Termes IGN] apprentissage profond
[Termes IGN] bande C
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Colombie-Britannique (Canada)
[Termes IGN] détection de changement
[Termes IGN] gestion des risques
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] incendie de forêt
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] Sydney (Nouvelle-Galles du Sud)
[Termes IGN] zone sinistréeRésumé : (auteur) Wildfires have coexisted with human societies for more than 350 million years, always playing an important role in affecting the Earth's surface and climate. Across the globe, wildfires are becoming larger, more frequent, and longer-duration, and tend to be more destructive both in lives lost and economic costs, because of climate change and human activities. To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even real-time. Satellite remote sensing enables cost-effective, accurate, and timely monitoring on the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8 and Sentinel-1/2 data opens the new era for global land surface monitoring, providing an opportunity to analyze wildfire impacts around the globe. The advances in both cloud computing and deep learning empower the automatic interpretation of spatio-temporal remote sensing big data on a large scale. The overall objective of this thesis is to investigate the potential of modern medium resolution earth observation data, especially Sentinel-1 C-Band synthetic aperture radar (SAR) data, in wildfire monitoring and develop operational and effective approaches for real-world applications. This thesis systematically analyzes the physical basis of earth observation data for wildfire applications, and critically reviews the available wildfire burned area mapping methods in terms of satellite data, such as SAR, optical, and SAR-Optical fusion. Taking into account its great power in learning useful representations, deep learning is adopted as the main tool to extract wildfire-induced changes from SAR and optical image time series. On a regional scale, this thesis has conducted the following four fundamental studies that may have the potential to further pave the way for achieving larger scale or even global wildfire monitoring applications. To avoid manual selection of temporal indices and to highlight wildfire-induced changes in burned areas, we proposed an implicit radar convolutional burn index (RCBI), with which we assessed the roles of Sentinel-1 C-Band SAR intensity and phase in SAR-based burned area mapping. The experimental results show that RCBI is more effective than the conventional log-ratio differencing approach in detecting burned areas. Though VV intensity itself may perform poorly, the accuracy can be significantly improved when phase information is integrated using Interferometric SAR (InSAR). On the other hand, VV intensity also shows the potential to improve VH intensity-based detection results with RCBI. By exploiting VH and VV intensity together, the proposed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the 2017 Thomas Fire and the 2018 Carr Fire. For the scenario of near real-time application, we investigated and demonstrated the potential Sentinel-1 SAR time series for wildfire progression monitoring with Convolutional Neural Networks (CNN). In this study, the available pre-fire SAR time series were exploited to compute temporal average and standard deviation for characterizing SAR backscatter behaviors over time and highlighting the changes with kMap. Trained with binarized kMap time series in a progression-wise manner, CNN showed good capability in detecting wildfire burned areas and capturing temporal progressions as demonstrated on three large and impactful wildfires with various topographic conditions. Compared to the pseudo masks (binarized kMap), CNN-based framework brought an 0.18 improvement in F1 score on the 2018 Camp Fire, and 0.23 on the 2019 Chuckegg Creek Fire. The experimental results demonstrated that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals. For continuous wildfire progression mapping, we proposed a novel framework of learning U-Net without forgetting in a near real-time manner. By imposing a temporal consistency restriction on the network response, Learning without Forgetting (LwF) allows the U-Net to learn new capabilities for better handling with newly incoming data, and simultaneously keep its existing capabilities learned before. Unlike the continuous joint training (CJT) with all available historical data, LwF makes U-Net learning not dependent on the historical training data any more. To improve the quality of SAR-based pseudo progression masks, we accumulated the burned areas detected by optical data acquired prior to SAR observations. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019-2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also found that the SAR cross-polarization ratio (VH/VV) can be very useful in highlighting burned areas when VH and VV have diverse temporal change behaviors. SAR-based change detection often suffers from the variability of the surrounding background noise, we proposed a Total Variation (TV)-regularized U-Net model to relieve the influence of SAR-based noisy masks. Considering the small size of labeled wildfire data, transfer learning was adopted to fine-tune U-Net from pre-trained weights based on the past wildfire data. We quantified the effects of TV regularization on increasing the connectivity of SAR-based areas, and found that TV-regularized U-Net can significantly increase the burned area mapping accuracy, bringing an improvement of 0.0338 in F1 score and 0.0386 in IoU score on the validation set. With TV regularization, U-Net trained with noisy SAR masks achieved the highest F1 (0.6904) and IoU (0.5295), while U-Net trained with optical reference mask achieved the highest F1 (0.7529) and IoU (0.6054) score without TV regularization. When applied on wildfire progression mapping, TV-regularized U-Net also worked significantly better than vanilla U-Net with the supervision of noisy SAR-based masks, visually comparable to optical mask-based results. On the regional scale, we demonstrated the effectiveness of deep learning on SAR-based and SAR-optical fusion based wildfire progression mapping. To scale up deep learning models and make them globally applicable, large-scale globally distributed data is needed. Considering the scarcity of labelled data in the field of remote sensing, weakly/self-supervised learning will be our main research directions to go in the near future. Note de contenu : 1- Introduction
2- Literature review
3- Study areas and data
4- Metodology
5- Results and discussionNuméro de notice : 28309 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics : RTK Stockholm : 2021 DOI : sans En ligne : http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1557429 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98130 Ensemble learning methods on the space of covariance matrices : application to remote sensing scene and multivariate time series classification / Sara Akodad (2021)
Titre : Ensemble learning methods on the space of covariance matrices : application to remote sensing scene and multivariate time series classification Type de document : Thèse/HDR Auteurs : Sara Akodad, Auteur ; Christian Germain, Directeur de thèse ; Lionel Bombrun, Directeur de thèse Editeur : Bordeaux : Université de Bordeaux Année de publication : 2021 Importance : 220 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée pour obtenir le grade de Docteur de l'Université de Bordeaux, Spécialité Automatique, Productique, Signal et Image, Ingénierie cognitiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse multivariée
[Termes IGN] Castanea sativa
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déformation temporelle dynamique (algorithme)
[Termes IGN] géométrie euclidienne
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] maladie phytosanitaire
[Termes IGN] matrice de covariance
[Termes IGN] processus gaussien
[Termes IGN] série temporelle
[Termes IGN] surveillance forestièreIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In view of the growing success of second-order statistics in classification problems, the work of this thesis has been oriented towards the development of learning methods in manifolds. Indeed, covariance matrices are symmetric positive definite matrices that live in a non-Euclidean space. It is therefore necessary to adapt the classical tools of Euclidean geometry to handle this type of data. To do that, we have proposed to exploit the log-Euclidean metric. This latter allows to project the set of covariance matrices on a tangent plane to the manifold defined at a reference point, classically chosen equal to the identity matrix, followed by a vectorization step to obtain the log-Euclidean representation. On this tangent plane, it is possible to define parametric Gaussian models as well as Gaussian mixture models. Nevertheless, this projection on a single tangent plane can induce distortions. In order to overcome this limitation, we have proposed a GMM model composed of several tangent planes, where the reference points are defined by the centers of each cluster.In view of the success of neural networks, in particular convolutional neural networks (CNNs), we have proposed two hybrid transfer learning approaches based on the covariance matrix computed locally and globally on the CNN convolutional layers’ outputs. The local approach relies on the covariance matrices extracted locally on the first layers of a CNN, which are then encoded by the Fisher vectors computed on their log-Euclidean representation, while for the global approach, a single covariance matrix is computed on the feature maps of the CNN deep layers. Moreover, in order to give more importance to the objects of interest present in the images, we proposed to use a covariance matrix weighted by the saliency information. Furthermore, in order to take advantage of both local and global aspects, these two approaches are subsequently combined in an ensemble strategy.On the other hand, the availability of multivariate time series has aroused the interest of the remote sensing community and more generally of machine learning researchers for the development of new learning strategies dedicated to supervised classification. In particular, methods based on the calculation of point-to-point distance between series. Moreover, two series belonging to the same class can evolve in different ways, which can induce temporal distortions (translation, compression, dilation, etc.). To avoid this, warping methods allow to align the time series. In order to extend this approach to time series of covariance matrices, while ensuring invariance to the re-parametrization of the series, we were interested in the TSRVF representation. In the same context, several ensemble methods have been proposed in the literature, including TCK, which relies on similarity computation to classify time series. We have proposed to extend this strategy to covariance matrices by introducing the SO-TCK approach which relies on the log-Euclidean representation of such matrices. Finally, the last axis of this thesis concerns the modeling of temporal trajectories of signals measured by the radar (Sentinel 1) and optical (Sentinel 2) sensors. In particular, we are interested in the forestry problem of the chestnut ink disease in the Montmorency forest. For this purpose, we developed classification and regression models to predict a health status score from the covariance matrix computed on multi-temporal radiometric attributes. Note de contenu : Introduction
1- Riemannian geometry and statistical modeling on the space of Symmetric Positive Definite (SPD) matrices
2- Ensemble learning approaches based on covariance pooling of CNN Features
3- Symmetric positive definite matrix time series classification
4- Forest health monitoring using Sentinel-1 and Sentinel-2 time series
Conclusions and perspectivesNuméro de notice : 28605 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Automatique, Productique, Signal et Image, Ingénierie cognitique : Bordeaux : 2021 Organisme de stage : IMS DOI : sans En ligne : https://tel.hal.science/tel-03484011 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99446 Evaluation of Sentinel-1 & 2 time series for the identification and characterization of ecological continuities, from wooded to crop-dominated landscapes / Audrey Mercier (2021)
Titre : Evaluation of Sentinel-1 & 2 time series for the identification and characterization of ecological continuities, from wooded to crop-dominated landscapes Type de document : Thèse/HDR Auteurs : Audrey Mercier, Auteur ; Laurence Hubert-Moy, Directeur de thèse ; Jacques Baudry, Directeur de thèse Editeur : Rennes : Université de Rennes 2 Année de publication : 2021 Importance : 305 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat présentée à l'Université de Rennes 2, Spécialité GéomatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] caractérisation
[Termes IGN] continuité écologique
[Termes IGN] cultures
[Termes IGN] données polarimétriques
[Termes IGN] forêt
[Termes IGN] habitat (nature)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] occupation du sol
[Termes IGN] paysage agricole
[Termes IGN] précision de la classification
[Termes IGN] série temporelle
[Termes IGN] utilisation du solIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Habitat loss is now considered one of the most serious threats to biodiversity. While many studies have focused on the circulation role of woodland features, very few have focused on the role of ecological continuities within agricultural mosaics. The objectives of this thesis were (1) to assess the combined use of Sentinel 1 and 2 time series to identify and characterize the elements of ecological continuities through land cover classifications and crop characterization in wooded and crop-dominated landscapes and (2) to estimate the impact of the spatio-temporal structuring of these landscape on biodiversity using metrics derived from Sentinel time series. The results showed that although S-2 data are more adapted than S-1 data to discriminate between land cover/land use types in wooded landscapes and phenological stages of wheat and rapeseed in crop-dominated landscapes, the combined use of S-2 and S-1 data improves their accuracy of the classifications, with S-1 data also showing a strong interest in cloudy areas. They also showed the interest of polarimetric indicators derived from S-1 data to characterize wheat and rapeseed crops. Finally, they highlighted the interest of the biophysical heterogeneity metrics derived from S-2 data to accurately estimate the distribution of carabid beetle species. The use of this metric, calculated with free images available everywhere on Earth, continuous and consistent from one site to another and from one type of crop to another,
should contribute to the study of the impact of ecological continuities on biodiversity.Note de contenu : General introduction
1. Ecological continuities from wooded to crop-dominated landscapes
2. The use of remote sensing imagery for the identification and characterization of ecological continuities
3. Study areas and data
4. Evaluation of Sentinel-1 and 2 time Series for land cover classification of forest–agriculture mosaics in temperate and tropical landscapes
5. Evaluation of Sentinel-1 and 2 time series for predicting wheat and rapeseed phenological stages
6. Evaluation of Sentinel-1 and 2 time series for estimating LAI and biomass of wheat and rapeseed crop types
7. Sentinel-2 images bring out functional biophysical heterogeneities in crop mosaics
General conclusion and perspectivesNuméro de notice : 26708 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Géomatique : Rennes 2 : 2021 Organisme de stage : Littoral, Environnement, Télédétection, Géomatique LETG nature-HAL : Thèse DOI : sans Date de publication en ligne : 14/10/2021 En ligne : https://tel.hal.science/tel-03377565 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99456 Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping / Mthembeni Mngadi in Geocarto international, vol 36 n° 1 ([01/01/2021])PermalinkMulti-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot (2021)PermalinkReal-time multimodal semantic scene understanding for autonomous UGV navigation / Yifei Zhang (2021)PermalinkSeasonal flow variability of Greenlandic glaciers : satellite observations and numerical modeling to study driving processes / Anna Derkacheva (2021)PermalinkPermalinkCartographie des cultures dans le périmètre du Loukkos (Maroc) : apport de la télédétection radar et optique / Siham Acharki in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)PermalinkCombination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia / Sanjiwana Arjasakusuma in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkA deep learning framework for matching of SAR and optical imagery / Lloyd Haydn Hughes in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkA generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery / Niangang Jiao in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkTextural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])Permalink