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Titre : Uncertainty in radar emitter classification and clustering Titre original : Gestion des incertitudes en identification des modes radar Type de document : Thèse/HDR Auteurs : Guillaume Revillon, Auteur ; Charles Soussen, Directeur de thèse ; A. Mohammad-Djafari, Directeur de thèse Editeur : Paris-Orsay : Université de Paris 11 Paris-Sud Centre d'Orsay Année de publication : 2019 Importance : 181 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l’Université Paris-Saclay préparée à l’Université Paris-Sud Sciences et Technologies de l’Information et de la Communication (STIC) Spécialité : Traitement du signal et des imagesLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] approximation
[Termes IGN] détection du signal
[Termes IGN] écho radar
[Termes IGN] émetteur
[Termes IGN] estimation bayesienne
[Termes IGN] inférence statistique
[Termes IGN] modèle de mélange multilinéaire
[Termes IGN] modulation du signal
[Termes IGN] probabilités
[Termes IGN] valeur aberranteIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In Electronic Warfare, radar signals identification is a supreme asset for decision making in military tactical situations. By providing information about the presence of threats, classification and clustering of radar signals have a significant role ensuring that countermeasures against enemies are well-chosen and enabling detection of unknown radar signals to update databases. Most of the time, Electronic Support Measures systems receive mixtures of signals from different radar emitters in the electromagnetic environment. Hence a radar signal, described by a pulse-to-pulse modulation pattern, is often partially observed due to missing measurements and measurement errors. The identification process relies on statistical analysis of basic measurable parameters of a radar signal which constitute both quantitative and qualitative data. Many general and practical approaches based on data fusion and machine learning have been developed and traditionally proceed to feature extraction, dimensionality reduction and classification or clustering. However, these algorithms cannot handle missing data and imputation methods are required to generate data to use them. Hence, the main objective of this work is to define a classification/clustering framework that handles both outliers and missing values for any types of data. Here, an approach based on mixture models is developed since mixture models provide a mathematically based, flexible and meaningful framework for the wide variety of classification and clustering requirements. The proposed approach focuses on the introduction of latent variables that give us the possibility to handle sensitivity of the model to outliers and to allow a less restrictive modelling of missing data. A Bayesian treatment is adopted for model learning, supervised classification and clustering and inference is processed through a variational Bayesian approximation since the joint posterior distribution of latent variables and parameters is untractable. Some numerical experiments on synthetic and real data show that the proposed method provides more accurate results than standard algorithms. Note de contenu : Introduction
1- State of the art and the selected approach
2- Continuous data
3- Mixed data
4- Temporal evolution data
5- Conclusion and perspectivesNuméro de notice : 25703 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du signal et des images : Paris 11 : 2019 Organisme de stage : Thales, GPI nature-HAL : Thèse DOI : sans Date de publication en ligne : 02/09/2019 En ligne : https://hal.science/tel-02275817 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94829 Undifferenced zenith tropospheric modeling and its application in fast ambiguity recovery for long-range network RTK reference stations / Dezhong Chen in GPS solutions, vol 23 n° 1 (January 2019)
[article]
Titre : Undifferenced zenith tropospheric modeling and its application in fast ambiguity recovery for long-range network RTK reference stations Type de document : Article/Communication Auteurs : Dezhong Chen, Auteur ; Shirong Ye, Auteur ; Caijun Xu, Auteur ; et al., Auteur Année de publication : 2019 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] Continuously Operating Reference Station network
[Termes IGN] correction troposphérique
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] propagation troposphérique
[Termes IGN] résidu
[Termes IGN] résolution d'ambiguïté
[Termes IGN] station de référence
[Termes IGN] station permanenteRésumé : (Auteur) A large number of continuously operating reference station (CORS) networks have been established around the world to support various high-precision navigation and positioning applications. However, the presence of significant tropospheric delays makes rapid ambiguity recovery for long inter-station baselines of network real-time kinematic (RTK) systems a major challenge. Since tropospheric delays are strongly temporally correlated over short periods, we propose an undifferenced (UD) zenith tropospheric prediction model to effectively correct tropospheric errors on the subsequent epoch measurements. Using 2-h sessions of the independent baselines in a CORS network, the ambiguities are easily and reliably resolved with the conventional ionospheric-free combination method. The derived double-differenced (DD), ionospheric-free residuals are then converted to UD residuals for each satellite and all stations. The UD residuals and the corresponding wet coefficients of each satellite are used to construct the zenith tropospheric model. The model is reconstructed every 5 min for each station. The slant tropospheric errors of observations within this period can be predicted using the established models. Seven independent baselines with an average length of 97 km are used to test the ambiguity recovery performance of the proposed method. The experimental results show that the proposed tropospheric prediction model can efficiently reduce the effects of slant tropospheric errors and improve the float solution of ambiguities. The average initialization time with the proposed method is less than 111.5 s, which is a 45% improvement with respect to the conventional approach. The proposed method was shown to be effective for fast ambiguity recovery of long-range baselines between reference stations. Numéro de notice : A2019-051 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-018-0815-x Date de publication en ligne : 02/01/2019 En ligne : https://doi.org/10.1007/s10291-018-0815-x Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92078
in GPS solutions > vol 23 n° 1 (January 2019)[article]Validating and comparing GNSS antenna calibrations / Ulla Kallio in Journal of geodesy, vol 93 n° 1 (January 2019)
[article]
Titre : Validating and comparing GNSS antenna calibrations Type de document : Article/Communication Auteurs : Ulla Kallio, Auteur ; Hannu Koivula, Auteur ; Sonja Lahtinen, Auteur ; Ville Nikkonen, Auteur ; Markku Poutanen, Auteur Année de publication : 2019 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] antenne GNSS
[Termes IGN] centre de phase
[Termes IGN] erreur systématique
[Termes IGN] étalonnage d'instrument
[Termes IGN] matrice de covariance
[Termes IGN] Metsähovi
[Termes IGN] modèle mathématique
[Termes IGN] positionnement cinématique
[Termes IGN] précision millimétrique
[Termes IGN] résidu
[Termes IGN] test de performanceRésumé : (auteur) GNSS antennas have no fixed electrical reference point. The variation of the phase centre is modelled and tabulated in antenna calibration tables, which include the offset vector (PCO) and phase centre variation (PCV) for each frequency according to the elevations and azimuths of the incoming signal. Used together, PCV and PCO reduce the phase observations to the antenna reference point. The remaining biases, called the residual offsets, can be revealed by circulating and rotating the antennas on pillars. The residual offsets are estimated as additional parameters when combining the daily GNSS network solutions with full covariance matrix. We present a procedure for validating the antenna calibration tables. The dedicated test field, called Revolver, was constructed at Metsähovi. We used the procedure to validate the calibration tables of 17 antennas. Tables from the IGS and three different calibration institutions were used. The tests show that we were able to separate the residual offsets at the millimetre level. We also investigated the influence of the calibration tables from the different institutions on site coordinates by performing kinematic double-difference baseline processing of the data from one site with different antenna tables. We found small but significant differences between the tables. Numéro de notice : A2019-031 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-018-1134-2 Date de publication en ligne : 22/03/2019 En ligne : https://doi.org/10.1007/s00190-018-1134-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91968
in Journal of geodesy > vol 93 n° 1 (January 2019) . - pp 1 - 18[article]Variational 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)
[article]
Titre : Variational learning of mixture wishart model for PolSAR image classification Type de document : Article/Communication Auteurs : Qian Wu, Auteur ; Biao Hou, Auteur ; Zaidao Wen, Auteur ; Licheng Jiao, Auteur Année de publication : 2019 Article en page(s) : pp 141 - 154 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification
[Termes IGN] image AIRSAR
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] loi de Wishart
[Termes IGN] optimisation (mathématiques)
[Termes IGN] polarimétrie radarRésumé : (Auteur) The phase difference, amplitude product, and amplitude ratio between two polarizations are important discriminators for terrain classification, which derives a significant statistical-distribution-based polarimetric synthetic aperture radar (PolSAR) image classification. Traditionally, statistical-distribution-based PolSAR image classification models pay attention to two aspects: searching for a suitable distribution to model certain PolSAR image and a satisfactory solution for the corresponding distribution model with samples in every terrain. Usually, the described distribution form is too complicated to build. Besides, inaccurate parameter estimation may lead to poor classification performance for PolSAR image. In order to refrain from this phenomenon, a variational thought is adopted for the statistical-distribution-based PolSAR classification method in this paper. First, a mixture Wishart model is built to model the PolSAR image to replace the complicated distribution for the PolSAR image. Second, a learning-based method is suggested instead of inaccurate point estimation of parameters to determine the distribution for every class in the mixture Wishart model. Finally, the proposed learning-based mixture Wishart model will be built as a variational form to realize a parametric model for PolSAR image classification. In the experiments, it will be proved that the class centers are easier to distinguish among different terrains learned from the proposed variational model. In addition, a classification performance on the PolSAR image is superior to the original point estimation Wishart model on both visual classification result and accuracy. Numéro de notice : A2019-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2852633 Date de publication en ligne : 16/08/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2852633 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92410
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 1 (January 2019) . - pp 141 - 154[article]Automatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)
[article]
Titre : Automatic building rooftop extraction from aerial images via hierarchical RGB-D priors Type de document : Article/Communication Auteurs : Shibiao Xu, Auteur ; Xingjia Pan, Auteur ; Er Li, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 7369 - 7387 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] détection du bâti
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] itération
[Termes IGN] scène urbaine
[Termes IGN] segmentation d'image
[Termes IGN] segmentation hiérarchique
[Termes IGN] toit
[Termes IGN] zone saillante 3DRésumé : (auteur) Accurate building rooftop extraction from high-resolution aerial images is of crucial importance in a wide range of applications. Owing to the varying appearance and large-scale range of scene objects, especially for building rooftops in different scales and heights, single-scale or individual prior-based extraction technique is insufficient in pursuing efficient, generic, and accurate extraction results. The trend toward integrating multiscale or several cue techniques appears to be the best way; thus, such integration is the focus of this paper. We first propose a novel salient rooftop detector integrating four correlative RGB-D priors (depth cue, uniqueness prior, shape prior, and transition surface prior) for improved rooftop extraction to address the preceding complex issues mentioned. Then, these correlative cues are computed from image layers created by our multilevel segmentation and further fused into the state-of-the-art high-order conditional random field (CRF) framework to locate the rooftop. Finally, an iterative optimization strategy is applied for high-quality solving, which can robustly handle varying appearance of building rooftops. Performance evaluations in the SZTAKI-INRIA benchmark data sets show that our method outperforms the traditional color-based algorithm and the original high-order CRF algorithm and its variants. The proposed algorithm is also evaluated and found to produce consistently satisfactory results for various large-scale, real-world data sets. Numéro de notice : A2018-558 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2850972 Date de publication en ligne : 26/07/2018 En ligne : http://dx.doi.org/10.1109/TGRS.2018.2850972 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91664
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 12 (December 2018) . - pp 7369 - 7387[article]Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach / Josselin Aval in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkEstimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations / Kun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkGénération d'observations pour la validation ou la comparaison de logiciels d'ajustement de mesures par moindres carrés / Stéphane Durand in XYZ, n° 157 (décembre 2018 - février 2019)PermalinkSuper-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkThe influence of artificial illumination of invar levelling rods / Štefan Rákay in Geodetski vestnik, vol 62 n° 4 (December 2018 - February 2019)PermalinkGlobal IWV trends and variability in atmospheric reanalyses and GPS observations / Ana-Claudia Bernardes Parracho in Atmospheric chemistry and physics, vol 18 n° 22 ([01/11/2018])PermalinkOn the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)PermalinkAutomated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)PermalinkDeep multi-task learning for a geographically-regularized semantic segmentation of aerial images / Michele Volpi in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkGPS satellite clock determination in case of inter-frequency clock biases for triple-frequency precise point positioning / Jiang Guo in Journal of geodesy, vol 92 n° 10 (October 2018)Permalink