Descripteur
Termes IGN > mathématiques > statistique mathématique > analyse de variance
analyse de variance
Commentaire :
Statistique,
Statistique mathématique. >> Analyse de covariance, Échantillonnage (statistique), Plan d'expérience. >>Terme(s) spécifique(s) : Analyse multivariée, Degré de liberté (physique), Écart type, Surface de réponse (statistique). Equiv. LCSH : Analysis of variance. Domaine(s) : 510. Voir aussi |
Documents disponibles dans cette catégorie (265)
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
A 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)
[article]
Titre : A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery Type de document : Article/Communication Auteurs : Farzaneh Dadrass Javan, Auteur ; Farhad Samadzadegan, Auteur ; Soroosh Mehravar, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 101 - 117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] affinage d'image
[Termes IGN] analyse de variance
[Termes IGN] fusion d'images
[Termes IGN] image Kompsat
[Termes IGN] image à haute résolution
[Termes IGN] image Geoeye
[Termes IGN] image Ikonos
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] image Pléiades-HR
[Termes IGN] image Quickbird
[Termes IGN] image Worldview
[Termes IGN] netteté
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] pouvoir de résolution spectraleRésumé : (auteur) Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images. Selecting an appropriate algorithm that maintains the spectral and spatial information content of input images is a challenging task. This review paper investigates a wide range of algorithms, including 41 methods. For this purpose, the methods were categorized as Component Substitution (CS-based), Multi-Resolution Analysis (MRA), Variational Optimization-based (VO), and Hybrid and were tested on a collection of 21 case studies. These include images from WorldView-2, 3 & 4, GeoEye-1, QuickBird, IKONOS, KompSat-2, KompSat-3A, TripleSat, Pleiades-1, Pleiades with the aerial platform, and Deimos-2. Neural network-based methods were excluded due to their substantial computational requirements for operational mapping purposes. The methods were evaluated based on four Spectral and three Spatial quality metrics. An Analysis Of Variance (ANOVA) was used to statistically compare the pan-sharpening categories. Results indicate that MRA-based methods performed better in terms of spectral quality, whereas most Hybrid-based methods had the highest spatial quality and CS-based methods had the lowest results both spectrally and spatially. The revisited version of the Additive Wavelet Luminance Proportional Pan-sharpening method had the highest spectral quality, whereas Generalized IHS with Best Trade-off Parameter with Additive Weights showed the highest spatial quality. CS-based methods generally had the fastest run-time, whereas the majority of methods belonging to MRA and VO categories had relatively long run times. Numéro de notice : A2021-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.001 Date de publication en ligne : 21/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96418
in ISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 101 - 117[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021011 SL Revue Centre de documentation Revues en salle Disponible 081-2021013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Stochastic models for geodesy and geoinformation science Type de document : Monographie Auteurs : Frank Neitzel, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 200 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03943-982-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] B-Spline
[Termes IGN] covariance
[Termes IGN] erreur de phase
[Termes IGN] incertitude de mesurage
[Termes IGN] interférométrie à très grande base
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle stochastique
[Termes IGN] phase GNSS
[Termes IGN] positionnement ponctuel précis
[Termes IGN] réseau géodésique
[Termes IGN] télémétrie laser aéroportéRésumé : (éditeur) In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena. Note de contenu : 1- Total least-squares collocation: An optimal estimation technique for the EIV-model with prior information
2- Weighted Total Least Squares (WTLS) Solutions for straight line fitting to 3D point data
3- Stochastic properties of confidence ellipsoids after least squares adjustment, derived from GUM analysis and Monte Carlo simulations
4- Mean shift versus variance inflation approach for outlier detection—A comparative study
5- A generic approach to covariance function estimation using ARMA-models
6- Evaluation of VLBI observations with sensitivity and robustness analyses
7- Variance reduction of sequential Monte Carlo approach for GNSS phase bias estimation
8- Automatic calibration of process noise matrix and measurement noise covariance for multi-GNSS precise point positioning
9- Elementary error model applied to terrestrial laser scanning measurements: Study case arch Dam Kops
10- On estimating the hurst parameter from least-squares residuals. Case study: Correlated terrestrial laser scanner range noiseNuméro de notice : 28459 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03943-982-9 En ligne : http://dx.doi.org/10.3390/books978-3-03943-982-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99044 Time-series analysis of massive satellite images : Application to earth observation / Alexandre Constantin (2021)
Titre : Time-series analysis of massive satellite images : Application to earth observation Titre original : Analyse de séries temporelles massives d'images satellitaires : Applications à la cartographie des écosystèmes Type de document : Thèse/HDR Auteurs : Alexandre Constantin, Auteur ; Stéphane Girard, Directeur de thèse ; Mathieu Fauvel, Directeur de thèse Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2021 Importance : 136 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse Pour obtenir le grade de Docteur de l'Université de Grenoble Alpes, Specialité : Mathématiques AppliquéesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multivariée
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification pixellaire
[Termes IGN] covariance
[Termes IGN] échantillonnage de données
[Termes IGN] écosystème
[Termes IGN] image Sentinel-MSI
[Termes IGN] processus gaussien
[Termes IGN] Python (langage de programmation)
[Termes IGN] régression
[Termes IGN] série temporelleIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis takes place in the context of the processing of the data from Sentinel-2 mission. This mission, initiated by the European Space Agency and launched in 2017, produces an unprecedented amount of Satellite Image Time-Series (SITS). Among the key analyses of these images, this thesis focuses on the classification task, i.e. land use or land cover maps that can be produced using spectro-temporal aspect of the Sentinel-2 SITS.Two main difficulties are identified in this thesis for the process of Sentinel-2 SITS. First, the unprecedented amount of data requires both scalable classifiers and code optimization techniques (such as parallel processing). Second, the acquisition noise (clouds, shadows) combined with the temporal aspect results in irregular and unevenly sampled time-series. Conventional approaches re-sample time-series to a set of time stamps, then they use machine learning techniques to classify vectors at a large-scale (national scale). The main disadvantage of this two-step processing approach is that it increases the number of operations applied to the SITS, implying a more difficult transition to massive amount of data. To a lower extent, the re-sampling step may slightly alter the temporal characteristics of the data.This thesis contributions are the following. We introduce a novel model-based approach with the ability to classify irregularly sampled time-series based on a mixture of multivariate Gaussian processes. A two-step approach has been used, by defining on one hand a model of uni-variate time-series, independent from the spectral wavelength point of view, then by considering on the second hand both spectral and temporal information from SITS. These models allow jointly a reconstruction of unobserved or noisy data. Estimation of both models has been implemented using a parallelized python code to be scalable to large-scale data-sets. The two models are evaluated numerically on Sentinel-2 SITS in terms of classification and reconstruction accuracy and are compared with conventional approaches. Analyses of the results illustrate the relevance of the two models and the benefit of using interpretable parametric models. Note de contenu : General Introduction
1- Satellite image time-series analysis and classification
2- Statistical modelling for time-series classification
3- Model-based classification for irregularly sampled time-series
4- Joint supervised classification and reconstruction of irregularly sampled satellite image times series
5- Mixture of multivariate gaussian processes for classification of irregularly sampled SITS
Conclusion and perspectivesNuméro de notice : 15280 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques Appliquées : Grenoble : 2021 Organisme de stage : Laboratoire Jean Kuntzmann DOI : sans En ligne : https://hal.science/tel-03682025 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101161 Error propagation in regional geoid computation using spherical splines, least-squares collocation, and Stokes’s formula / Vegard Ophaug in Journal of geodesy, vol 94 n° 12 (December 2020)
[article]
Titre : Error propagation in regional geoid computation using spherical splines, least-squares collocation, and Stokes’s formula Type de document : Article/Communication Auteurs : Vegard Ophaug, Auteur ; Christian Gerlach, Auteur Année de publication : 2020 Article en page(s) : n° 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] altitude
[Termes IGN] collocation par moindres carrés
[Termes IGN] covariance
[Termes IGN] erreur
[Termes IGN] fonction spline
[Termes IGN] formule de Stokes
[Termes IGN] géoïde local
[Termes IGN] propagation d'erreurRésumé : (auteur) Current International Association of Geodesy efforts within regional geoid determination include the comparison of different computation methods in the quest for the “1-cm geoid.” Internal (formal) and external (empirical) approaches to evaluate geoid errors exist, and ideally they should agree. Spherical radial base functions using the spline kernel (SK), least-squares collocation (LSC), and Stokes’s formula are three commonly used methods for regional geoid computation. The three methods have been shown to be theoretically equivalent, as well as to numerically agree on the millimeter level in a closed-loop environment using synthetic noise-free data (Ophaug and Gerlach in J Geod 91:1367–1382, 2017. https://doi.org/10.1007/s00190-017-1030-1PANIST). This companion paper extends the closed-loop method comparison using synthetic data, in that we investigate and compare the formal error propagation using the three methods. We use synthetic uncorrelated and correlated noise regimes, both on the 1-mGal (=10−5 ms−2) level, applied to the input data. The estimated formal errors are validated by comparison with empirical errors, as determined from differences of the noisy geoid solutions to the noise-free solutions. We find that the error propagations of the methods are realistic in both uncorrelated and correlated noise regimes, albeit only when subjected to careful tuning, such as spectral band limitation and signal covariance adaptation. For the SKs, different implementations of the L-curve and generalized cross-validation methods did not provide an optimal regularization parameter. Although the obtained values led to a stabilized numerical system, this was not necessarily equivalent to obtaining the best solution. Using a regularization parameter governed by the agreement between formal and empirical error fields provided a solution of similar quality to the other methods. The errors in the uncorrelated regime are on the level of ∼5 mm and the method agreement within 1 mm, while the errors in the correlated regime are on the level of ∼10 mm, and the method agreement within 5 mm. Stokes’s formula generally gives the smallest error, closely followed by LSC and the SKs. To this effect, we note that error estimates from integration and estimation techniques must be interpreted differently, because the latter also take the signal characteristics into account. The high level of agreement gives us confidence in the applicability and comparability of formal errors resulting from the three methods. Finally, we present the error characteristics of geoid height differences derived from the three methods and discuss them qualitatively in relation to GNSS leveling. If applied to real data, this would permit identification of spatial scales for which height information is preferably derived by spirit leveling or GNSS leveling. Numéro de notice : A2020-784 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01443-y Date de publication en ligne : 27/11/2020 En ligne : https://doi.org/10.1007/s00190-020-01443-y Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96528
in Journal of geodesy > vol 94 n° 12 (December 2020) . - n° 120[article]Geostatistical analysis and mitigation of the atmospheric phase screens in Ku-band terrestrial radar interferometric observations of an alpine glacier / Simone Baffelli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : Geostatistical analysis and mitigation of the atmospheric phase screens in Ku-band terrestrial radar interferometric observations of an alpine glacier Type de document : Article/Communication Auteurs : Simone Baffelli, Auteur ; Othmar Frey, Auteur ; Irena Hajnsek, Auteur Année de publication : 2020 Article en page(s) : pp 7533 - 7556 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Alpes
[Termes IGN] analyse spatio-temporelle
[Termes IGN] bande Ku
[Termes IGN] covariance
[Termes IGN] erreur de phase
[Termes IGN] géostatistique
[Termes IGN] glacier
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] série temporelle
[Termes IGN] vapeur d'eau
[Termes IGN] variogrammeRésumé : (auteur) Terrestrial radar interferometry (TRI) can measure displacements at high temporal resolution, potentially with high accuracy. An application of this method is the observation of the surface flow velocity of steep, fast-flowing aglaciers. For these observations, the main factor limiting the accuracy of TRI observations is the spatial and temporal variabilities in the distribution of atmospheric water vapor content, causing a phase delay [atmospheric phase screen (APS)] whose magnitude is similar to the displacement phase. This contribution presents a geostatistical analysis of the spatial and temporal behaviors of the APS in Ku-Band TRI. The analysis is based on the assumption of a separable spatiotemporal covariance structure, which is tested empirically using variogram analysis. From this analysis, spatial and temporal APS statistics are estimated and used in a two-step procedure combining regression-Kriging with generalized least squares (GLS) inversion to estimate a velocity time-series. The performance of this method is evaluated by cross-validation using phase observations on stable scatterers. This analysis shows a considerable reduction in residual phase variance compared with the standard approach of combining the linear models of APS stratification and interferogram stacking. Numéro de notice : A2020-675 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976656 Date de publication en ligne : 13/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96166
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7533 - 7556[article]Semi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)PermalinkThe impact of drought on total ozone flux in a mountain Norway spruce forest / Thomas Agyei in Journal of forest science, vol 66 n° 7 (juillet 2020)PermalinkEstimation of the F2 generation segregation variance and relationships among growth, frost damage, and bud break in coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) wide-crosses / Andy Benowicz in Annals of Forest Science, Vol 77 n° 2 (June 2020)PermalinkAccounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocities / Clément Benoist in Journal of geodynamics, vol 135 (April 2020)PermalinkMonitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys / Ashutosh Tiwari in Geocarto international, vol 35 n° 5 ([01/04/2020])PermalinkPredictive mapping with small field sample data using semi‐supervised machine learning / Fei Du in Transactions in GIS, Vol 24 n° 2 (April 2020)PermalinkA single-receiver geometry-free approach to stochastic modeling of multi-frequency GNSS observables / Baocheng Zhang in Journal of geodesy, vol 94 n°4 (April 2020)PermalinkEstimation of variance and spatial correlation width for fine-scale measurement error in digital elevation model / Mikhail L. Uss in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkA breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals / Olivier Bock in Statistics and Computing, vol 29 n° 1 (February 2020)PermalinkAutocovariance-based perceptual textural features corresponding to human visual perception / N. Abbadeni (2020)PermalinkDevelopment of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability / Annarosa Quarello (2020)PermalinkPermalinkFlowering acceleration in native Brazilian tree species for genetic conservation and breeding / Gleidson Guilherme Caldas Mende in Annals of forest research, Vol 63 n° 1 (January - June 2020)PermalinkA new segmentation method for the homogenisation of GNSS-derived IWV time-series / Annarosa Quarello (2020)PermalinkCombination of GRACE monthly gravity fields on the normal equation level / Ulrich Meyer in Journal of geodesy, vol 93 n° 9 (September 2019)PermalinkThe Iranian height datum offset from the GBVP solution and spirit-leveling/gravimetry data / Amir Ebadi in Journal of geodesy, vol 93 n° 8 (August 2019)PermalinkGenetic diversity and structure of Silver fir (Abies alba Mill.) at the south-eastern limit of its distribution range / Maria Teodosiu in Annals of forest research, vol 62 n° 2 (June - December 2019)PermalinkHelmert-VCE-aided fast-WTLS approach for global ionospheric VTEC modelling using data from GNSS, satellite altimetry and radio occultation / Andong Hu in Journal of geodesy, vol 93 n°6 (June 2019)PermalinkSeasonal pattern in time series of variances of GPS residual errors Anova estimates / Darko Anđić in Geodetski vestnik, vol 63 n° 2 (June - August 2019)PermalinkCoastline extraction from SAR images using robust ridge tracing / Dailiang Wang in Marine geodesy, vol 42 n° 3 (May 2019)Permalink