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Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series / Kevin Gobron in Journal of geodesy, vol 96 n° 7 (July 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 7 (July 2022) . - n° 46 Titre : Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series Type de document : Article/Communication Auteurs : Kevin Gobron, Auteur ; Paul Rebischung , Auteur ; Olivier de Viron, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] analyse de variance [Termes IGN] bruit blanc [Termes IGN] fréquence [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] modèle de Gauss-Markov [Termes IGN] modèle stochastique [Termes IGN] série temporelle [Termes IGN] vitesse Résumé : (auteur) Understanding and modelling the properties of the stochastic variations in geodetic time series is crucial to obtain realistic uncertainties for deterministic parameters, e.g., long-term velocities, and helpful in characterizing non-modelled processes. With the increasing span of geodetic time series, it is expected that additional observations would help better understand the low-frequency properties of these stochastic variations. In the meantime, recent studies evidenced that the choice of the functional model for the time series biases the assessment of these low-frequency stochastic properties. In particular, frequent offsets in position time series can hinder the evaluation of the noise level at low frequencies and prevent the detection of possible random-walk-type variability. This study investigates the ability of the Maximum Likelihood Estimation (MLE) method to correctly retrieve low-frequency stochastic properties of geodetic time series in the presence of frequent offsets. We show that part of the influence of offsets reported by previous studies results from the MLE method estimation biases. These biases occur even when all offset epochs are correctly identified and accounted for in the trajectory model. They can cause a dramatic underestimation of deterministic parameter uncertainties. We show that one can avoid biases using the Restricted Maximum Likelihood Estimation (RMLE) method. Yet, even when using the RMLE method or equivalent, adding offsets to the trajectory model inevitably blurs the estimated low-frequency properties of geodetic time series by increasing low-frequency stochastic parameter uncertainties more than other stochastic parameters. Numéro de notice : A2022-519 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01634-9 Date de publication en ligne : 29/06/2022 En ligne : https://doi.org/10.1007/s00190-022-01634-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101072 [article]

Effect of label noise in semantic segmentation of high resolution aerial images and height data / Arabinda Maiti in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 275 - 282 Titre : Effect of label noise in semantic segmentation of high resolution aerial images and height data Type de document : Article/Communication Auteurs : Arabinda Maiti, Auteur ; Sander J. Oude Elberink, Auteur ; M. George Vosselman, Auteur Année de publication : 2022 Article en page(s) : pp 275 - 282 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bruit (théorie du signal) [Termes IGN] données altimétriques [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image à très haute résolution [Termes IGN] image aérienne [Termes IGN] orthoimage [Termes IGN] segmentation sémantique Résumé : (auteur) The performance of deep learning models in semantic segmentation is dependent on the availability of a large amount of labeled data. However, the influence of label noise, in the form of incorrect annotations, on the performance is significant and mostly ignored. This is a big concern in remote sensing applications, wherein acquired datasets are spatially limited, labeling is done by domain experts with possible sources of high inter-and intra-observer variability leading to erroneous predictions. In this paper, we first simulate the label noise while conducting experiments on two different datasets with very high-resolution aerial images, height data, and inaccurate labels, responsible for the training of deep learning models. We then focus on the effect of these noises on the model performance. Different classes respond differently to the label noise. The typical size of an object belonging to a class is a crucial factor regarding the class-specific performance of the model trained with erroneous labels. Errors caused by relative shifts of labels are the most influential label errors. The model is generally more tolerant of the random label noise than other label errors. It has been observed that the accuracy gets reduced by at least 3% while 5% of label pixels are erroneous. In this regard, our study provides a new perspective of evaluating and quantifying the propagation of label noise in the model performance that is indeed important for adopting reliable semantic segmentation practices. Numéro de notice : A2022-434 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-275-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-275-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100741 [article]

Multi-frequency quadrifilar helix antennas for cm-accurate GNSS positioning / Lambert Wanninger in Journal of applied geodesy, vol 16 n° 1 (January 2022)  

Public [article]  inJournal of applied geodesy > vol 16 n° 1 (January 2022) . - pp 25 - 35 Titre : Multi-frequency quadrifilar helix antennas for cm-accurate GNSS positioning Type de document : Article/Communication Auteurs : Lambert Wanninger, Auteur ; Melanie Thiemig, Auteur ; Walker Frevert, Auteur Année de publication : 2022 Article en page(s) : pp 25 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] antenne GNSS [Termes IGN] bruit (théorie du signal) [Termes IGN] étalonnage d'instrument [Termes IGN] fréquence multiple [Termes IGN] phase GNSS [Termes IGN] positionnement par GNSS [Termes IGN] précision centimétrique [Termes IGN] signal GNSS [Termes IGN] trajet multiple Résumé : (auteur) For a few years now, GNSS multi-frequency quadrifilar helix antennas (QHA) are available to be used for precise GNSS applications. We performed test measurements with two types of multi-frequency QHA and compared them with a geodetic patch antenna. Although code and carrier phase noise and high-frequent multipath was determined to be larger as compared to the geodetic antenna, the fast-static horizontal coordinate accuracies are on the same level and demonstrate cm-accuracy capability. One of the QHA types exhibited an increased susceptibility to near-field multipath effects which resulted in a degraded accuracy of the vertical coordinate component. Numéro de notice : A2022-054 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2021-0042 Date de publication en ligne : 15/09/2021 En ligne : https://doi.org/10.1515/jag-2021-0042 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99449 [article]

A multipath and thermal noise joint error characterization and exploitation for low-cost GNSS PVT estimators in urban environment / Eustachio Roberto Matera (2022)  

Public Titre : A multipath and thermal noise joint error characterization and exploitation for low-cost GNSS PVT estimators in urban environment Type de document : Thèse/HDR Auteurs : Eustachio Roberto Matera, Auteur ; Carl Milner, Directeur de thèse ; Axel Javier Garcia Pena, Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2022 Importance : 348 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse délivré par l'Institut National Polytechnique de Toulouse en Informatique et Télécommunication Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] bruit thermique [Termes IGN] correction du trajet multiple [Termes IGN] corrélation temporelle [Termes IGN] filtre de Kalman [Termes IGN] rapport signal sur bruit [Termes IGN] récepteur GNSS [Termes IGN] signal GPS [Termes IGN] trajet multiple [Termes IGN] zone urbaine dense Index. décimale : THESE Thèses et HDR Résumé : (auteur) Achieving an accurate localization is a significant challenge for low-cost GNSS devices in dense urban areas. The main limitations are encountered in the urban canyons, consisting in a reduced satellite signal availability and a positioning estimation error due to the impact of Line-of-Sight and Non Line-of-Sight multipath phenomenon. This PhD study allows to understand the impact of the multipath phenomenon on the low-cost GNSS receivers and to prove the need of accurate assessment of the multipath error model affecting the GNSS measurements, especially in urban environment. It consists in the investigation, characterization, and finally, exploitation of the multipath error components affecting the pseudorange and pseudorange-rate measurements, of a single frequency, dual constellation GNSS receiver in the urban environment, operating with GPS L1 C/A and Galileo E1 OS signals. The first goal consists in providing a set of methodologies able to identify, isolate and characterize the multipath error components from the measurements under test. However, considering that the isolation of the multipath error is a complex operation due to the superimposed effects of multipath and thermal noise, the final method consists of isolating the joint contribution of multipath and thermal noise components. The isolated multipath and thermal noise error components are firstly classified depending the corresponding received signal /0 values, and, secondly, statistically characterized by means of Probability Density Function, sample mean and sample variance. Also, the temporal and spatial correlation properties of the isolated error components are calculated by means of a methodology which estimates the temporal correlations as a function of the receiver speed. In addition, an image processing methodology based on the application of a sky-facing fish-eye camera provides the determination of an empirical /0 threshold equal to 35 dB-Hz used to qualitatively identify the Non Line- Of-Sight and Line-Of-Sight received signal reception states. The resulting errors are characterized by a nonsymmetrical, positive biased PDF for a /0 lower than 35 dBHz, while they are characterized by a symmetrical and zero-centred PDF for a /0 higher than 35 dB-Hz. Correlation times for pseudoranges are ranged from around 5s for static and very low speed dynamics to around 1s for high-speed dynamics. Correlation times for pseudorange-rates ranged from around 0.5s for static and very low speed dynamics to around 0.2s for high-speed dynamics, due to the data-rate limitations. The second goal consists in exploiting the multipath and thermal noise error models and the LOS/NLOS received signal reception state estimation in a low-complex EKF-based architecture to improve the accuracy of the PVT estimates. This is obtained by implementing some techniques based on the measurement weighting approach to take into account the statistical properties of the error under exam and by the application of a time differenced architecture design to exploit the temporal correlation properties. Positioning performance of the tested solutions surpassed the performances of a simple EKF architecture and are comparable to the performances of a uBlox M8T receiver. Note de contenu : 1- Introduction 2- GNSS architecture 3- GNSS receiver processing 4- Multipath effects on the GNSS receiver tracking performances 5- Multipath characterization methodologies 6- Multipath characterization results 7- Proposed extended Kalman Filter Algorithm 8- Conclusions and recommandations for future works Numéro de notice : 15272 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse française Note de thèse : Thèse de Doctorat: en Informatique et Télécommunication : Toulouse :2022 Organisme de stage : (TELECOM-ENAC) DOI : sans En ligne : http://www.theses.fr/2022INPT0030 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100992

Deep learning-based image de-raining using discrete Fourier transformation / Prasen Kumar Sharma in The Visual Computer, vol 37 n° 8 (August 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 8 (August 2021) . - pp 2083 - 2096 Titre : Deep learning-based image de-raining using discrete Fourier transformation Type de document : Article/Communication Auteurs : Prasen Kumar Sharma, Auteur ; Sathisha Basavaraju, Auteur ; Arijit Sur, Auteur Année de publication : 2021 Article en page(s) : pp 2083 - 2096 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bruit (théorie du signal) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] décomposition d'image [Termes IGN] filtrage du bruit [Termes IGN] pluie [Termes IGN] transformation de Fourier Résumé : (auteur) Single image rain streak removal is a well-explored topic in the field of computer vision. The de-raining problem is modeled as an image decomposition task where a rainy image is decomposed into rain-free background image and rain streek map. Unlike most of the existing de-raining methods, this paper attempts to decompose the rainy image in the frequency domain. The idea is inspired by pseudo-periodic characteristics of the noise signal (here the rain streaks) which leave some traces in the frequency domain, and the same can be utilized to predict the noise signal. In this paper, a deep learning-based rain streak prediction model is proposed which learns in discrete Fourier transform Oppenheim and Schafer (Discrete-Time Signal Processing, Prentice Hall, Upper Saddle River, 1989) domain. To the best of our knowledge, this is the first approach where compressed domain coefficients are directly used as input to a deep convolutional neural network. The proposed model has been tested on publicly available synthetic datasets Fu et al. (in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.186, Yang et al. (in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.183), Yeh et al. (in: 2015 IEEE International Conference on Consumer Electronics-Taiwan, 2015. https://doi.org/10.1109/ICCE-TW.2015.7216999) and results are found to be comparable with the state of the art methods in the spatial domain. The presented analysis and study have an obvious indication to extend transform domain input to train the deep learning architecture especially image de-noising like problems. Numéro de notice : A2021-597 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01971-w Date de publication en ligne : 16/09/2020 En ligne : https://doi.org/10.1007/s00371-020-01971-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98226 [article]

Impact of the third frequency GNSS pseudorange and carrier phase observations on rapid PPP convergences / Jiang Guo in GPS solutions, vol 25 n° 2 (April 2021)  

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Cluster-based empirical tropospheric corrections applied to InSAR time series analysis / Kyle Dennis Murray in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

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Denoising Sentinel-1 extra-wide mode cross-polarization images over sea ice / Yan Sun in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

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G-band radar for humidity and cloud remote sensing / Ken B. Cooper in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

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Improving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)  

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Applied signal processing / Sadasivan Puthusserypady (2021)  

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Characteristics of seasonal variations and noises of the daily double-difference and PPP solutions / Kamil Maciuk in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

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GNSS/5G Hybridization for Urban Navigation / Anne-Marie Tobie (2021)  

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Statistical analysis of vertical land motions and sea level measurements at the coast / Kevin Gobron (2021)  

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Télédétection hyperspectrale pour l’identification et la caractérisation de minéraux industriels / Ronan Rialland (2021)  

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