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Stochastic model reliability in GNSS baseline solution / Aviram Borko in Journal of geodesy, vol 95 n° 2 (February 2021)
[article]
Titre : Stochastic model reliability in GNSS baseline solution Type de document : Article/Communication Auteurs : Aviram Borko, Auteur ; Gilad Even-Tzur, Auteur Année de publication : 2021 Article en page(s) : n° 20 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données GNSS
[Termes IGN] double différence
[Termes IGN] fiabilité des données
[Termes IGN] ligne de base
[Termes IGN] matrice de covariance
[Termes IGN] modèle stochastique
[Termes IGN] résolution d'ambiguïté
[Termes IGN] test statistique
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) GNSS observations stochastic model influences all subsequent stages of data processing, from the possibility to reach the optimal parameters estimation, to the reliability and quality control of the solution. Nowadays, an uncontrolled use of GNSS stochastic models is common for both data processing and simulation missions, especially in commercial GNSS software packages. As a result, the variance–covariance matrices that are derived in the processing are inadequate and cause incorrect interpretations of the results. A proper method to evaluate the reliability of the stochastic model is needed to reflect the confidence level in statistic testing and simulation mission efforts. In this contribution, a novel method for evaluating the statistical nature of GNSS stochastic model is presented. The method relies on the deterministic nature of the integer ambiguity variable to examine and express the expected multinormal distribution of the double-difference adjustment results. The suggested method was used with a controlled experiment and 24 h of observations data to investigate how the statistical nature of the stochastic model is affected by different baseline lengths. The results indicate that as the baseline length increases, the stochastic model is less predictable and exposed to irregularities in the observation’s precision. Additionally, the reliability of the integer ambiguity resolution success rate (SR) was tested as part of the stochastic model evaluation. The results show a dramatic degradation in the SR prediction level when using an inadequate stochastic model, which suggests using extra caution when handling this parameter unless high-confidence reliable stochastic model is available. Numéro de notice : A2021-136 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01472-1 Date de publication en ligne : 31/01/2021 En ligne : https://doi.org/10.1007/s00190-021-01472-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97009
in Journal of geodesy > vol 95 n° 2 (February 2021) . - n° 20[article]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 Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the bay of Bengal / Raghu Nadimpalli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
[article]
Titre : Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the bay of Bengal Type de document : Article/Communication Auteurs : Raghu Nadimpalli, Auteur ; Akhil Srivastava, Auteur ; V. S. Prasad, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6945 - 6957 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Bengale, golfe du
[Termes IGN] cyclone
[Termes IGN] image INSAT-VHRR
[Termes IGN] interpolation
[Termes IGN] matrice de covariance
[Termes IGN] modèle de transfert radiatif
[Termes IGN] précipitation
[Termes IGN] prévision météorologique
[Termes IGN] radiance
[Termes IGN] zone intertropicaleRésumé : (auteur) This is the first study concerning the assimilation of the INSAT-3D/3DR radiance in the Hurricane Weather Research and Forecasting (HWRF) model and assesses its credibility to improve track, intensity, and precipitation forecasts of tropical cyclone (TC) Titli that occurred over the Bay of Bengal (BoB), which showed rapid intensification (RI) and weakening through its lifetime. The inbuilt Gridpoint Statistical Interpolation (GSI) method is used with a 3-D variational (3DVAR) configuration. Three sets of numerical experiments such as control (CNTL) (no assimilation), Global Telecommunication System (GTS) (observations from GTS network), and INSAT-3D/3DR (INSAT-3D/3DR sounder radiance data and GTS observations) were carried out with seven different initializations. The radiance analysis reproduced the initial vortex and the prominent synoptic scale features associated with TC Titli. The average root-mean-square errors (RMSE) of the analysis were relatively lower in the INSAT-3D/3DR compared to the CNTL and GTS. The HWRF performance is enhanced for track simulation, with improvements in mean landfall position errors by 40%–70% and 26%–52% for the INSAT-3D/3DR and GTS runs, respectively. The assimilation of radiance data has a positive impact on the simulation of warm core and thermodynamic structures, which has led to a more accurate intensity prediction (by 30–47%) over the CNTL. The assimilation run could realistically simulate the RI and weakening phases of the TC. A cold dry air intrusion is also observed when associated with the weakening. The study highlights the need to incorporate INSAT-3D/3DR radiances for improved TC predictions over the BoB basin. Numéro de notice : A2020-587 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2978211 Date de publication en ligne : 25/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2978211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95915
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 6945 - 6957[article]Stochastic modeling for VRS network-based GNSS RTK with residual interpolation uncertainty / Thanate Jongrujinan in Journal of applied geodesy, vol 14 n° 3 (July 2020)
[article]
Titre : Stochastic modeling for VRS network-based GNSS RTK with residual interpolation uncertainty Type de document : Article/Communication Auteurs : Thanate Jongrujinan, Auteur ; Chalermchon Satirapod, Auteur Année de publication : 2020 Article en page(s) : pp 317 – 325 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] correction atmosphérique
[Termes IGN] incertitude de position
[Termes IGN] interpolation
[Termes IGN] matrice de covariance
[Termes IGN] modèle stochastique
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement par GNSS
[Termes IGN] précision du positionnement
[Termes IGN] résolution d'ambiguïté
[Termes IGN] station virtuelle de référence
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) The key concept of the virtual reference station (VRS) network-based technique is to use the observables of multiple reference stations to generate the network corrections in the form of a virtual reference station at a nearby user’s location. Regarding the expected positioning accuracy, the novice GNSS data processing strategies have been adopted in the server-side functional model for mitigating distance-dependent errors including atmospheric effects and orbital uncertainty in order to generate high-quality virtual reference stations. In addition, the realistic stochastic model also plays an important role to take account of the unmodelled error in the rover-side processing. The results of our previous study revealed that the minimum norm quadratic unbiased estimation (MINQUE) stochastic model procedure can improve baseline component accuracy and integer ambiguity reliability, however, it requires adequate epoch length in a solution to calculate the elements of the variance-covariance matrix. As a result, it may not be suitable for urban environment where the satellite signal interruptions take place frequently, therefore, the ambiguity resolution needs to be resolved within the limited epochs. In order to address this limitation, this study proposed the stochastic model based on using the residual interpolation uncertainty (RIU) as the weighting schemes. This indicator reflects the quality of network corrections for any satellite pair at a specific rover position and can be calculated on the epoch-by-epoch basis. The comparison results with the standard stochastic model indicated that the RIU-weight model produced slightly better positioning accuracy but increased significant level of the ambiguity resolution successful rate. Numéro de notice : A2020-398 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0007 Date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.1515/jag-2020-0007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95433
in Journal of applied geodesy > vol 14 n° 3 (July 2020) . - pp 317 – 325[article]A point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
[article]
Titre : A point cloud feature regularization method by fusing judge criterion of field force Type de document : Article/Communication Auteurs : Xijiang Chen, Auteur ; Qing Liu, Auteur ; Kegen Yu, Auteur Année de publication : 2020 Article en page(s) : pp 2994 - 3006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse vectorielle
[Termes IGN] arbre BSP
[Termes IGN] détection de contours
[Termes IGN] échantillonnage
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] matrice de covariance
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation du bâti
[Termes IGN] niveau de gris (image)
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] spline cubique
[Termes IGN] traitement d'image
[Termes IGN] transformation de Hough
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Point cloud boundary is an important part of the surface model. The traditional feature extraction method has slow speed and low efficiency and only achieves the boundary feature points. Hence, the point cloud feature regularization is proposed to obtain the boundary lines based on the fast extraction of feature points in this article. First, an improved $k$ - $d$ tree method is used to search the $k$ neighbors of sampling point. Then, the sampling point and its $k$ neighbors are used as the reference points set to fit a microcut plane and project to the plane. The local coordinate system is established on the microcut plane to convert 3-D into 2-D. The boundary feature points are identified by judging criterion of field force and then are sorted and connected according to the vector deflected angle and distance. Finally, the boundary lines are smoothed by the improved cubic B-spline fitting method. Experiments show that the proposed method can extract the boundary feature points quickly and efficiently, and the mean error of boundary lines is 0.0674 mm and the standard deviation is 0.0346 mm, which has high precision. This proposed method was also successfully applied to feature extraction and boundary fitting of Xinyi teaching building of the Wuhan University of Technology. Numéro de notice : A2020-230 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946326 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2946326 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94968
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 2994 - 3006[article]Deep SAR-Net: learning objects from signals / Zhongling Huang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkPermalinkPermalinkIdentification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)PermalinkThe certitude of a global sea level acceleration during the satellite altimeter era / Huseyin Baki Iz in Journal of geodetic science, vol 10 n° 1 (January 2020)PermalinkRobust M–M unscented Kalman filtering for GPS/IMU navigation / Cheng Yang in Journal of geodesy, vol 93 n° 8 (August 2019)PermalinkAn improved robust Kalman filtering strategy for GNSS kinematic positioning considering small cycle slips / Wanke Liu in Advances in space research, vol 63 n° 9 (1 May 2019)PermalinkOn constrained integrated total Kalman filter for integrated direct geo-referencing / Vahid Mahboub in Survey review, vol 51 n° 364 (January 2019)PermalinkValidating and comparing GNSS antenna calibrations / Ulla Kallio in Journal of geodesy, vol 93 n° 1 (January 2019)PermalinkAtmospheric artifacts correction with a covariance-weighted linear model over mountainous regions / Zhongbo Hu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)Permalink