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Termes IGN > mathématiques > statistique mathématique
statistique mathématique
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biométrie,
échantillonnage (statistique), probabilité, statistique. >>Terme(s) spécifique(s) : analyse de régression, analyse de variance, analyse des données, analyse multivariée, analyse séquentielle, calcul d'erreur, carré latin, corrélation (statistique), efficacité asymptotique (statistique), fonction pseudo-aléatoire, loi des grands nombres, modèle linéaire (statistique), modèle non linéaire (statistique), moindre carré, physique statistique, plan d'expérience, rang et sélection (statistique), rupture (statistique), SAS (logiciel), série chronologique, statistique non paramétrique, statistique robuste, tableau de contingence, test d'hypothèses (statistique), statistique stellaire. Equiv. LCSH : Mathematical statistics. Domaine(s) : 510. |
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Estimating regional soil moisture with synergistic use of AMSR2 and MODIS images / Majid Rahimzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)
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[article]
Titre : Estimating regional soil moisture with synergistic use of AMSR2 and MODIS images Type de document : Article/Communication Auteurs : Majid Rahimzadegan, Auteur ; Arash Davari, Auteur ; Ali Sayadi, Auteur Année de publication : 2021 Article en page(s) : pp 649-660 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Advanced Microwave Scanning Radiometer
[Termes IGN] coefficient de corrélation
[Termes IGN] humidité du sol
[Termes IGN] image Aqua-AMSR
[Termes IGN] image Terra-MODIS
[Termes IGN] indice d'humidité
[Termes IGN] Iran
[Termes IGN] polarisation
[Termes IGN] réflectance du solRésumé : (Auteur) Soil moisture content (SMC), product of Advanced Microwave Scanning Radiometer 2 (AMSR2), is not at an adequate level of accuracy on a regional scale. The aim of this study is to introduce a simple method to estimate SMC while synergistically using AMSR2 and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements with a higher accuracy on a regional scale. Two MODIS products, including daily reflectance (MYD021) and nighttime land surface temperature (LST) products were used. In 2015, 1442 in situ SMC measurements from six stations in Iran were used as ground-truth data. Twenty models were evaluated using combinations of polarization index (PI), index of soil wetness (ISW), normalized difference vegetation index (NDVI), and LST. The model revealed the best results using a quadratic combination of PI and ISW, a linear form of LST, and a constant value. The overall correlation coefficient, root-mean-square error, and mean absolute error were 0.59, 4.62%, and 3.01%, respectively. Numéro de notice : A2021-673 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00085 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.14358/PERS.20-00085 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98835
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 9 (September 2021) . - pp 649-660[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021091 SL Revue Centre de documentation Revues en salle Disponible Gaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds / Longjie Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)
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Titre : Gaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds Type de document : Article/Communication Auteurs : Longjie Ye, Auteur ; Ka Zhang, Auteur ; Wen Xiao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 615 - 630 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme de filtrage
[Termes IGN] classification barycentrique
[Termes IGN] courbure
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fonction spline d'interpolation
[Termes IGN] Kappa de Cohen
[Termes IGN] lasergrammétrie
[Termes IGN] modèle numérique de terrain
[Termes IGN] processus gaussien
[Termes IGN] semis de pointsRésumé : (Auteur) This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges. Numéro de notice : A2021-671 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.20-00080 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.14358/PERS.87.20-00080 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98820
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 9 (September 2021) . - pp 615 - 630[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021091 SL Revue Centre de documentation Revues en salle Disponible
[article]
Titre : Geoglam, l'agriculture par satellite Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2021 Article en page(s) : pp 17 - 17 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] blé (céréale)
[Termes IGN] carte d'occupation du sol
[Termes IGN] Glycine max
[Termes IGN] image à haute résolution
[Termes IGN] Leaf Area Index
[Termes IGN] maïs (céréale)
[Termes IGN] modèle dynamique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] observation de la Terre
[Termes IGN] Oryza (genre)
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (Auteur) Des satellites pour la sécurité alimentaire et la transparence du marché agricole Numéro de notice : A2021-579 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 13/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98400
in Géomètre > n° 2194 (septembre 2021) . - pp 17 - 17[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 063-2021081 RAB Revue Centre de documentation En réserve L003 Disponible GIScience integrated with computer vision for the examination of old engravings and drawings / Motti Zohar in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)
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Titre : GIScience integrated with computer vision for the examination of old engravings and drawings Type de document : Article/Communication Auteurs : Motti Zohar, Auteur ; Ilan Shimshoni, Auteur Année de publication : 2021 Article en page(s) : pp 1703 - 1724 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carte en 3D
[Termes IGN] carte profonde
[Termes IGN] dessin
[Termes IGN] extraction de données
[Termes IGN] Israël
[Termes IGN] Jérusalem
[Termes IGN] patrimoine culturel
[Termes IGN] paysage
[Termes IGN] Ransac (algorithme)
[Termes IGN] système d'information géographique
[Termes IGN] vision par ordinateurRésumé : (auteur) Landscape reconstructions and deep maps are two major approaches in cultural heritage studies. In general, they require the use of historical visual sources such as maps, graphic artworks, and photographs presenting areal scenes, from which one can extract spatial information. However, photographs, the most accurate and reliable source for scenery reconstruction, are available only from the second half of the 19th century onward. Thus, for earlier periods one can rely only on old artworks. Nevertheless, the accuracy and inclusiveness of old artworks are often questionable and must be verified carefully. In this paper, we use GIScience methods with computer-vision capabilities to interrogate old engravings and drawings as well as to develop a new approach for extracting spatial information from these scenic artworks. We have inspected four old depictions of Jerusalem and Tiberias (Israel) created between the 17th and 19th centuries. Using visibility analysis and a RANSAC algorithm we identified the locations of the artists when they drew the artworks and evaluated the accuracy of their final products. Finally, we re-projected 3D map digitized features onto the drawing canvases, thus embedding features not originally drawn. These were then identified, enabling potential extraction of the spatial information they may reflect. Numéro de notice : A2021-592 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1874957 Date de publication en ligne : 25/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1874957 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98213
in International journal of geographical information science IJGIS > vol 35 n° 9 (September 2021) . - pp 1703 - 1724[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021091 SL Revue Centre de documentation Revues en salle Disponible Influence of aperiodic non-tidal atmospheric and oceanic loading deformations on the stochastic properties of global GNSS vertical land motion time series / Kevin Gobron in Journal of geophysical research : Solid Earth, vol 126 n° 9 (September 2021)
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Titre : Influence of aperiodic non-tidal atmospheric and oceanic loading deformations on the stochastic properties of global GNSS vertical land motion time series Type de document : Article/Communication Auteurs : Kevin Gobron, Auteur ; Paul Rebischung , Auteur ; Michel Van Camp, Auteur ; Alain Demoulin, Auteur ; Olivier de Viron, Auteur
Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° e2021JB022370 Note générale : bibliographie
This study has been financially supported by the Direction Générale de l’Armement (DGA), the Nouvelle-Aquitaine region, and the Centre National des Etudes Spatiales (CNES) as an application of the geodesy missions. This research was also supported by the Brain LASUGEO project entitled ”monitoring LAnd SUbsidence caused by Groundwater exploitation through gEOdetic measurements” funded by the Belgian Sciences Policy. This is IPGP contribution number 4214.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] coordonnées GNSS
[Termes IGN] déformation verticale de la croute terrestre
[Termes IGN] erreur systématique
[Termes IGN] résidu
[Termes IGN] série temporelle
[Termes IGN] station permanente
[Termes IGN] surcharge atmosphérique
[Termes IGN] surcharge océaniqueRésumé : (auteur) Monitoring vertical land motions (VLMs) at the level of 0.1 mm/yr remains one of the most challenging scientific applications of global navigation satellite systems (GNSS). Such small rates of change can result from climatic and tectonic phenomena, and their detection is important to many solid Earth-related studies, including the prediction of coastal sea-level change and the understanding of intraplate deformation. Reaching a level of precision allowing to detect such small signals requires a thorough understanding of the stochastic variability in GNSS VLM time series. This paper investigates how the aperiodic part of non-tidal atmospheric and oceanic loading (NTAOL) deformations influences the stochastic properties of VLM time series. Using the time series of over 10,000 stations, we describe the impact of correcting for NTAOL deformation on 5 complementary metrics, namely: the repeatability of position residuals, the power-spectrum of position residuals, the estimated time-correlation properties, the corresponding velocity uncertainties, and the spatial correlation of the residuals. We show that NTAOL deformations cause a latitude-dependent bias in white noise plus power-law model parameter estimates. This bias is significantly mitigated when correcting for NTAOL deformation, which reduces velocity uncertainties at high latitudes by 70%. Therefore, removing NTAOL deformation before the statistical analysis of VLM time series might help to detect subtle VLM signals in these areas. Our spatial correlation analysis also reveals a seasonality in the spatial correlation of the residuals, which is reduced after removing NTAOL deformation, confirming that NTAOL is a clear source of common-mode errors in GNSS VLM time series. Numéro de notice : A2021-783 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Autre URL associée : vers HAL Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2021JB022370 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.1029/2021JB022370 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98954
in Journal of geophysical research : Solid Earth > vol 126 n° 9 (September 2021) . - n° e2021JB022370[article] PermalinkLarge-area inventory of species composition using airborne laser scanning and hyperspectral data / Hans Ole Ørka in Silva fennica, vol 55 n° 4 (September 2021)
PermalinkModeling in forestry using mixture models fitted to grouped and ungrouped data / Eric K. Zenner in Forests, vol 12 n° 9 (September 2021)
PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
PermalinkA new approach for the development of grid models calculating tropospheric key parameters over China / Ge Zhu in Remote sensing, vol 13 n° 17 (September-1 2021)
PermalinkProtection naturelle contre la submersion, apport de l'intelligence artificielle / Antoine Mury in Cartes & Géomatique, n° 245-246 (septembre - décembre 2021)
PermalinkRegularized regression: A new tool for investigating and predicting tree growth / Stuart I. Graham in Forests, vol 12 n° 9 (September 2021)
PermalinkSensitivity of change-point detection and trend estimates to GNSS IWV time series properties / Khanh Ninh Nguyen in Atmosphere, vol 12 n° 9 (September 2021)
PermalinkSentinel-1 sensitivity to soil moisture at high incidence angle and the impact on retrieval over seasonal crops / Davide Palmisano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
PermalinkStochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network / Jussi Leinonen in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
PermalinkTropospheric and range biases in Satellite Laser Ranging / Mateusz Drożdżewski in Journal of geodesy, vol 95 n° 9 (September 2021)
PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])
PermalinkUtilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)
PermalinkVariational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
PermalinkVectorial integer bootstrapping: flexible integer estimation with application to GNSS / Peter J.G. Teunissen in Journal of geodesy, vol 95 n° 9 (September 2021)
PermalinkMonitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico / Yao Gao in Geocarto international, vol 36 n° 15 ([15/08/2021])
PermalinkSpatial patterns of living and dead small trees in subalpine Norway spruce forest reserves in Switzerland / Eva Bianchi in Forest ecology and management, vol 494 (August-15 2021)
PermalinkUnsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])
PermalinkAutomated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN) / Zhenbang Hao in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
PermalinkBackground segmentation in multicolored illumination environments / Nikolas Ladas in The Visual Computer, vol 37 n° 8 (August 2021)
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