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Termes IGN > imagerie > image radar > image radar moirée
image radar moiréeSynonyme(s)Interferogramme ;image SAR ;Image rso ;Image radar interférométrique Image par radar à antenne synthétiqueVoir aussi
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Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)
[article]
Titre : Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest Type de document : Article/Communication Auteurs : Aline Bernarda Debastiani, Auteur ; Carlos Roberto Sanquetta, Auteur ; Ana Paula Dalla Corte, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 109 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Amazonie
[Termes IGN] apprentissage automatique
[Termes IGN] arbre aléatoire
[Termes IGN] bande C
[Termes IGN] biomasse aérienne
[Termes IGN] Brésil
[Termes IGN] forêt tropicale
[Termes IGN] fusion d'images
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] réseau neuronal convolutifRésumé : (auteur) The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (AGB) in a high-biomass tropical ecosystem. This study was carried out in Jamari National Forest, located in the Brazilian Amazon. The response variable was AGB (Mg/ha) estimated from airborne laser surveys. The following treatments were considered as model predictors: 1) Sentinel-1 Sigma 0 at VV and VH polarizations; 2) (1) plus Sentinel-1 textural metrics; 3) (2) plus Sentinel-2 bands and derived vegetation indices (LAI, RVI, SAVI, NDVI).Our modeling design estimated the relative importance of SAR vs. optical variables in explaining AGB. The modeling was performed with twelve machine-learning algorithms including, neural network and regression tree. The addition of texture and optical data provided a noticeable improvement (3%) over models with SAR backscatter only. The best model performance was achieved with the Random Tree algorithm. Our results demonstrate the potential of freely-available SAR data and machine learning for mapping AGB in tropical ecosystems. Numéro de notice : A2019-335 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15287/afr.2018.1267 Date de publication en ligne : 30/07/2019 En ligne : http://dx.doi.org/10.15287%2Fafr.2018.1267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93349
in Annals of forest research > vol 62 n° 1 (January - June 2019) . - pp 109 - 122[article]Evaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)
Titre : Evaluation of time-series SAR and optical images for the study of winter land-use Type de document : Thèse/HDR Auteurs : Julien Denize, Auteur ; Eric Pottier, Directeur de thèse ; Laurence Hubert-Moy, Directeur de thèse Editeur : Rennes : Université de Rennes 1 Année de publication : 2019 Importance : 274 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Rennes 1, Mathématiques et Sciences et Technologies de l'Information et de la Communication, Spécialité Signal Image Vision & GéomatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] agriculture
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données polarimétriques
[Termes IGN] hiver
[Termes IGN] image à haute résolution
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] nébulosité
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelle
[Termes IGN] télédétection spatiale
[Termes IGN] utilisation du solIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The study of winter land-use is a major challenge in order to preserve and improve the quality of soils and surface water. However, knowledge of the spatio-temporal dynamics associated with winter land-use remains a challenge for the scientific community. In this context, the objective of this study is to evaluate the potential of time series of high spatial resolution optical and SAR images for the study of winter land-use at a local and regional scale. For that purpose, a methodology has been established to: (i) determine the most suitable classification method for identifying winter land-use ; (ii) compare Sentinel-1 SAR and Sentinel-2 optical images; (iii) define the most suitable SAR configuration by comparing three image time-series (Alos-2, Radarsat-2 and Sentinel-1).The results first of all highlighted the interest of the Random Forest classification algorithm to discriminate at a fine scale the different types of land use in winter. Secondly, they showed the value of Sentinel-2 data for mapping winter land-use at a local and regional scale. Finally, they determined that a dense time series of Sentinel-1 images was the most appropriate SAR configuration to identify winter land-use. In general, while this thesis has shown that Sentinel-2 data are best suited to studying land use in winter, SAR images are of great interest in regions with significant cloud cover, dense Sentinel-1 time-series having being defined as the most efficient. Note de contenu : General Introduction
1- Winter land-use: concepts, data and methods
2- Classification procedure for the winter land-use study at a local scale
3- SAR configuration for the study of winter land-use at a local scale
4- The study of winter land-use at a regional scale
General conclusion and perspectivesNuméro de notice : 25710 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Signal Image Vision & Géomatique : Rennes1 : 2019 Organisme de stage : Institut d’Electronique et de Télécommunication de Rennes nature-HAL : Thèse DOI : sans En ligne : https://hal.science/tel-02510333/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94858 Global observations of ocean surface winds and waves using spaceborne synthetic aperture radar measurements / Huimin Li (2019)
Titre : Global observations of ocean surface winds and waves using spaceborne synthetic aperture radar measurements Type de document : Thèse/HDR Auteurs : Huimin Li, Auteur Editeur : Institut Mines-Télécom Atlantique IMT Atlantique Année de publication : 2019 Importance : 163 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat de l'Ecole Nationale Supérieure des Mines-Telecom Atlantique Bretagne Pays de la Loire-IMT Atlantique, en Sciences de la Mer et du littoral : Spécialité : Océanographie, Physique et EnvironnementLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] angle d'incidence
[Termes IGN] étalonnage des données
[Termes IGN] fonction de transfert de modulation
[Termes IGN] houle
[Termes IGN] image Envisat-ASAR
[Termes IGN] image Gaofen
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] image Sentinel-SAR
[Termes IGN] océan
[Termes IGN] phénomène météorologique
[Termes IGN] polarisation croisée
[Termes IGN] radar à antenne synthétique
[Termes IGN] rapport signal sur bruit
[Termes IGN] tempête
[Termes IGN] vague
[Termes IGN] ventIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Spaceborne synthetic aperture radar (SAR) has been demonstrated invaluable in observing the global ocean winds and waves. SAR images acquired by multiple sensors are employed, including Sentinel-1(S-1), Envisat/ASAR, Gaofen-3 and Radarsat-2. This thesis reviews the commonly used SAR parameters (NRCS and azimuth cutoff) in the first part. A series of calibration steps are required to obtain a proper NRCS and assessment of NRCS is carried out for S-1wave mode (WV). It turns out that WV is poorly calibrated and is thus re-calibrated to obtain accurate NRCS. Azimuth cut off is demonstrated to be complementary to NRCS and can account for the sea state impact on the wind retrieval. Based on the available fully polarimetric SAR products, azimuth cut off is found to vary greatly with polarizations. The present SAR mapping transformation is sufficient to interpret the co-polarized azimuth cut off, while not for the cross-polarization. With the limitations of SAR imaging in mind, a new parameter is proposed and defined based on the SAR image cross-spectra, termed as MACS. The imaginary part of MACS is found to be a signed quantity relative to the wind direction. Given this dependence, an independent wind retrieval algorithm is expected to benefit. The magnitude of MACS is able to aid for estimate of modulation function of SAR mapping. In addition, MACS also gives promising results regarding the global wave studies. The global signatures of MACS at various wave lengths are well representative of the winds distributions, spatially and seasonally. MACS of long waves shows greater values over the storm tracks while the shorter waves are mostly within the trader winds. These results are expected to help evaluate the model outputs and complement further studies of the global wave spectral climate. Data continuity in the coming 10 years shall extend the study towards longer duration. Note de contenu : I- Background and study of the existing SAR parameters
Chap. 1 - Background
Chap. 2 - SAR imaging of the ocean surface
Chap. 3 - Status and challenges in SAR winds
Chap. 4 - Azimuth cutoff of polarimetric SAR images
II- A new SAR parameter and its applications in wind/wave study
Chap. 5 - A new SAR parameter: MACS and its directionality
Chap. 6 - Statistics of MACS magnitude and derived RAR MTF
Chap. 7 - Investigation of global ocean waves using SAR MACS
Chap. 8 - Conclusion and perspectivesNuméro de notice : 25729 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Océanographie, Physique et Environnement : Ecole nationale supérieure Mines-Télécom Atlantique : 2019 Organisme de stage : Ifremer nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02164506/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94895 Joint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)
Titre : Joint analysis of SAR and optical satellite images time series for grassland event detection Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , Auteur Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par réseau neuronal
[Termes IGN] cohérence des données
[Termes IGN] détection d'événement
[Termes IGN] détection de changement
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Mâcon
[Termes IGN] prairie
[Termes IGN] puits de carboneRésumé : (auteur) Throughout Europe, grasslands are a major component of the landscape comprising 40% of agricultural land. Permanent Grassland (PM) means land used to grow herbaceous forage crops naturally (self-seeded) or through cultivation (sown) and that has not been included in the crop rotation of the holding for five years or more. PM are major ecosystems associated with high biodiversity which provide a wide range of ecosystem services (e.g. carbon sequestration, water quality, flood and erosion control). Grasslands have an important carbon storage capacity which is valuable for climate protection. Different studies have demonstrated that grassland managements such as grazing or mowing can cause significant effects on carbon storage in soils. Identifying and mapping grassland management practices over time can thus have important impact on climate studies. Remote sensing allows a synoptic and regular monitoring through systematic acquisitions of Earth Observation imagery. The emergence of free and easily Sentinel's satellite data provided by the European Copernicus program, offers new possibilities for grassland monitoring. Sentinel-1 (51) and Sentinel-2 (52) missions acquire radar and optical satellite image time series at high temporal resolution and fine spatial resolution. They fully match the requirements both for yearly and real-time monitoring. In this work, we target to jointly exploit both data sources to dynamically detect mowing events (MowEve) on permanent grasslands. Thematic related analysis of the datasets will highlight strengths and weaknesses of both optical and radar imagery. (i) 52 appears efficient for MowEve detection, with significant variations in the vegetation status that can be easily detected in the spectral signal extracted from the time series of images. But the temporal revisit of 52 although nominally 5 days is often reduced even by half due to the frequent cloud cover (ii) SAR images acquisitions being independent of illumination conditions or cloud cover allows for systematic acquisitions and revisit rate of 6 days. Data consistency makes S1 data essential during fast phenomena such as MowEve. Yet, radar data appears very sensitive to soil moisture, precipitations and geometrical properties making interpretation of their time series more challenging. MowEve detection being weakly supervised, the proposed methodology relies on applying traditional change detection strategies on a low-level fused 51 and S2 data representation. Recurrent Neural Networks will be trained to derive yearly or real-time synthetic 52 vegetation indices from both 52 and S1 observations. Furthermore, through attention mechanisms, our proposed RNN architecture will be able to take into account external data (climate, clouds, topography, etc.) so as to dynamically weight at parcel-level the contribution of optical and radar images. Such method will contribute to obtain dense temporal optical profiles without missing data and compatible with MowEve detection. An experimental evaluation will be carried out on a test site covering an area of 110x110 Km in France (Macon region). Object-oriented analysis will be presented based on permanent grasslands derived from the Land Parcel Identification System. The proposed approach will be compared with traditional MowEve methods essentially based on thresholding independently the different modalities. Numéro de notice : C2019-067 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97022
Titre de série : Learning to understand remote sensing images, 1 Titre : Volume 1 Type de document : Monographie Auteurs : Qi Wang, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 426 p. ISBN/ISSN/EAN : 978-3-03897-685-1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse texturale
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat
[Termes IGN] image radar moirée
[Termes IGN] réseau neuronal convolutifRésumé : (Editeur) With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field. Numéro de notice : 26301A Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-685-1 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.3390/books978-3-03897-685-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95033 PermalinkMicrowave indices from active and passive sensors for remote sensing applications / Emanuele Santi (2019)PermalinkMultitemporal SAR images denoising and change detection : applications to Sentinel-1 data / Weiying Zhao (2019)PermalinkPermalinkPermalinkPermalinkToward global soil moisture monitoring with sentinel-1 : harnessing assets and overcoming obstacles / Bernhard Bauer-Marschallinger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkUrban deformation monitoring using persistent scatterer Interferometry and SAR tomography / Michele Crosetto (2019)PermalinkVariational learning of mixture wishart model for PolSAR image classification / Qian Wu in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkPolarization orientation angle and polarimetric SAR scattering characteristics of steep terrain / Jong-Sen Lee in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkPotential of Sentinel-1 data for monitoring temperate mixed forest phenology / Pierre-Louis Frison in Remote sensing, vol 10 n° 12 (December 2018)PermalinkInvestigation of the success of monitoring slow motion landslides using Persistent Scatterer Interferometry and GNSS methods / K.O. Hastaoglu in Survey review, vol 50 n° 363 (September 2018)PermalinkA new scheme for urban impervious surface classification from SAR images / Hongsheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkA statistical approach to preprocess and enhance C-band SAR images in order to detect automatically marine oil slicks / Zhour Najoui in IEEE Transactions on geoscience and remote sensing, vol 56 n° 5 (May 2018)PermalinkTowards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkEstimated location of the seafloor sources of marine natural oil seeps from sea surface outbreaks : A new "source path procedure" applied to the northern Gulf of Mexico / Zhour Najoui in Marine and Petroleum Geology, Vol 91 (March 2018)PermalinkPermalinkCaractérisation et qualification de Modèles Numériques de Surfaces (MNS) - Analyse de la cohérence avec des masques d’eau / Guillaume Sutter (2018)PermalinkCartographie des déformations de surface sur l’île de Taiwan par interférométrie RADAR Sentinel-1 / Miloud Fekaouni (2018)PermalinkDétection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018)Permalink