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Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)
[article]
Titre : Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints Type de document : Article/Communication Auteurs : Li Liu, Auteur ; Luping Xu, Auteur ; Houzhang Fang, Auteur Année de publication : 2020 Article en page(s) : pp 1777 - 1789 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse bivariée
[Termes IGN] analyse comparative
[Termes IGN] filtrage du bruit
[Termes IGN] image infrarouge
[Termes IGN] intensité lumineuse
[Termes IGN] interpolation polynomiale
[Termes IGN] itération
[Termes IGN] optimisation (mathématiques)
[Termes IGN] programmation par contraintes
[Termes IGN] texture d'imageRésumé : (Auteur) Infrared (IR) images are often contaminated by obvious intensity bias and stripes, which severely affect the visual quality and subsequent applications. It is challenging to eliminate simultaneously the mixed nonuniformity noise without blurring the fine-image details in low-textured IR images. In this article, we present a new model for simultaneous intensity bias correction and destriping through introducing two sparsity constraints. One is that model fit on the intensity bias should be as accurate as possible. A bivariate polynomial model is built to characterize the global smoothness of the intensity bias. The other constraint is that the unidirectional variational sparse model can concisely represent the direction characteristic of stripe noise. A computationally efficient numerical algorithm based on split Bregman iteration is used to solve the complex optimization problem. The proposed method is fundamentally different from the existing denoising techniques and simultaneously estimates the sharp image, intensity bias, and stripe components. Significant improvement on image quality is achieved on both simulated and real studies. Both qualitative and quantitative comparisons with the state-of-the-art correction methods demonstrate its superiority. Numéro de notice : A2020-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2948601 Date de publication en ligne : 18/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2948601 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94663
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1777 - 1789[article]Smoothing and predicting celestial pole offsets using a Kalman filter and smoother / Jolanta Nastula in Journal of geodesy, Vol 94 n°3 (March 2020)
[article]
Titre : Smoothing and predicting celestial pole offsets using a Kalman filter and smoother Type de document : Article/Communication Auteurs : Jolanta Nastula, Auteur ; T. Mike Chin,, Auteur ; Richard S. Gross, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] filtre de Kalman
[Termes IGN] International Earth Rotation Service
[Termes IGN] lissage de données
[Termes IGN] mission spatiale
[Termes IGN] mouvement du pôle
[Termes IGN] nutation
[Termes IGN] orientation de la Terre
[Termes IGN] précession
[Termes IGN] radar JPL
[Termes IGN] rotation de la Terre
[Termes IGN] série temporelleRésumé : (auteur) It has been recognized since the early days of interplanetary spaceflight that accurate navigation requires taking into account changes in the Earth’s rotation. In the 1960s, tracking anomalies during the Ranger VII and VIII lunar missions were traced to errors in the Earth orientation parameters. As a result, Earth orientation calibration methods were improved to support the Mariner IV and V planetary missions. Today, accurate Earth orientation parameters are used to track and navigate every interplanetary spaceflight mission. The approach taken at JPL (Jet Propulsion Laboratory) to provide the interplanetary spacecraft tracking and navigation teams with the UT1 and polar motion parameters that they need is based upon the use of a Kalman filter to combine past measurements of these parameters and predict their future evolution. A model was then used to provide the nutation/precession components of the Earth’s orientation. As a result, variations caused by the free core nutation were not taken into account. But for the highest accuracy, these variations must be considered. So JPL recently developed an approach based upon the use of a Kalman filter and smoother to provide smoothed and predicted celestial pole offsets (CPOs) to the interplanetary spacecraft tracking and navigation teams. The approach used at JPL to do this and an evaluation of the accuracy of the predicted CPOs is given here. For assessing the quality of JPL’s nutation predictions, we compare the time series of dX, dY provided by JPL with the predictions obtained from the IERS Rapid Service/Prediction Centre. Our results confirmed that the approach recently developed by JPL can be used for the successful nutation prediction. In particular, we show that after 90 days of prediction, the estimated errors are 43% lower for dX and 33% lower for dY than in the case of the official IERS products, and an average improvement is 19% and 22% for dX and dY, respectively. Numéro de notice : A2020-156 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01349-9 Date de publication en ligne : 15/02/2020 En ligne : https://doi.org/10.1007/s00190-020-01349-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94806
in Journal of geodesy > Vol 94 n°3 (March 2020)[article]Assessment of inner reliability in the Gauss-Helmert model / Andreas Ettlinger in Journal of applied geodesy, vol 14 n° 1 (January 2020)
[article]
Titre : Assessment of inner reliability in the Gauss-Helmert model Type de document : Article/Communication Auteurs : Andreas Ettlinger, Auteur ; Hans Neuner, Auteur Année de publication : 2020 Article en page(s) : pp 13 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie
[Termes IGN] coefficient de corrélation
[Termes IGN] compensation par moindres carrés
[Termes IGN] détection d'erreur
[Termes IGN] erreur systématique
[Termes IGN] fiabilité des données
[Termes IGN] filtre de Kalman
[Termes IGN] modèle d'erreur
[Termes IGN] modèle de Gauss-Helmert
[Termes IGN] valeur aberranteRésumé : (auteur) In this contribution, the minimum detectable bias (MDB) as well as the statistical tests to identify disturbed observations are introduced for the Gauss-Helmert model. Especially, if the observations are uncorrelated, these quantities will have the same structure as in the Gauss-Markov model, where the redundancy numbers play a key role. All the derivations are based on one-dimensional and additive observation errors respectively offsets which are modeled as additional parameters to be estimated. The formulas to compute these additional parameters with the corresponding variances are also derived in this contribution. The numerical examples of plane fitting and yaw computation show, that the MDB is also in the GHM an appropriate measure to analyze the ability of an implemented least-squares algorithm to detect if outliers are present. Two sources negatively influencing detectability are identified: columns close to the zero vector in the observation matrix B and sub-optimal configuration in the design matrix A. Even if these issues can be excluded, it can be difficult to identify the correct observation as being erroneous. Therefore, the correlation coefficients between two test values are derived and analyzed. Together with the MDB these correlation coefficients are an useful tool to assess the inner reliability – and therefore the detection and identification of outliers – in the Gauss-Helmert model. Numéro de notice : A2020-040 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2019-0013 Date de publication en ligne : 19/10/2019 En ligne : https://doi.org/10.1515/jag-2019-0013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94511
in Journal of applied geodesy > vol 14 n° 1 (January 2020) . - pp 13 - 28[article]INS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)
[article]
Titre : INS/GNSS integration using recurrent fuzzy wavelet neural networks Type de document : Article/Communication Auteurs : Parisa Doostdar, Auteur ; Jafar Keighobadi, Auteur ; Mohammad Ali Hamed, Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification floue
[Termes IGN] couplage GNSS-INS
[Termes IGN] données GNSS
[Termes IGN] filtre de Kalman
[Termes IGN] interruption du signal
[Termes IGN] ondelette
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau neuronal récurrent
[Termes IGN] vitesse
[Vedettes matières IGN] Traitement de données GNSSRésumé : (Auteur) In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages. Numéro de notice : A2020-019 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0942-z Date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1007/s10291-019-0942-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94458
in GPS solutions > vol 24 n° 1 (January 2020)[article]Kalman filtering with state constraints applied to multi-sensor systems and georeferencing / Sören Vogel (2020)
Titre : Kalman filtering with state constraints applied to multi-sensor systems and georeferencing Type de document : Thèse/HDR Auteurs : Sören Vogel, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 856 Importance : 144 p. ISBN/ISSN/EAN : 978-3-7696-5268-0 Note générale : bibliographie
Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Universität Hannover ISSN 0174-1454, Nr. 364, 2020Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] contrainte d'intégrité
[Termes IGN] convolution (signal)
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] géoréférencement direct
[Termes IGN] positionnement cinématique
[Termes IGN] programmation par contraintes
[Termes IGN] semis de pointsRésumé : (auteur) Active research on the development of autonomous vehicles has been carried out for several years now. However, some significant challenges still need to be solved in this context. Particularly relevant is the constant guarantee and assurance of the integrity of such autonomous systems. In order to ensure safe manoeuvring in the direct environment of humans, an accurate, precise, reliable and continuous determination of the vehicle’s position and orientation is mandatory. In geodesy, this process is also referred to as georeferencing with respect to a superordinate earth-fixed coordinate system. Especially for complex inner-city areas, there are no fully reliable methods available so far. The otherwise suitable and therefore common Global Navigation Satellite System (GNSS) observations can fail in urban canyons. However, this fact does not only apply exclusively to autonomous vehicles but can generally also be transferred to any kinematic Multi-Sensor System (MSS) operating within challenging environments. Especially in geodesy, there are many MSSs, which require accurate and reliable georeferencing regardless of the environment. This is indispensable for derived subsequent products, such as highly accurate three-dimensional point clouds for 3D city models or Building Information Modelling (BIM) applications. The demand for new georeferencing methods under aspects of integrity also involves the applicability of big data. Modern sensors for capturing the environment, e.g. laser scanners or cameras, are becoming increasingly cheaper and also offer higher information density and accuracy. For many kinematic MSSs, this change leads to a steady increase in the amount of acquired observation data. Many of the currently methods used are not suitable for processing such amounts of data, and instead, they only use a random subset. Besides, big data also influences potential requirements with regard to possible real-time applications. If there is no excessive computing power available to take into account the vast amounts of observation data, recursive methods are usually recommended. In this case, an iterative estimation of the requested quantities is performed, whereby the comprehensive total data set is divided into several individual epochs. If the most recent observations are successively available for each epoch, a filtering algorithm can be applied. Thus, an efficient estimation is carried out and, with respect to a comprehensive overall adjustment, generally larger observation sets can be considered. However, such filtering algorithms exist so far almost exclusively for explicit relations between the available observations and the requested estimation quantities.
If this mathematical relationship is implicit, which is certainly the case for several practical issues, only a few methods exist or, in the case of recursive parameter estimation, none at all. This circumstance is accompanied by the fact that the combination of implicit relationships with constraints regarding the parameters to be estimated has not yet been investigated at all. In this thesis, a versatile filter algorithm is presented, which is valid for explicit and for implicit mathematical relations as well. For the first time, methods for the consideration of constraints are given, especially for implicit relations. The developed methodology will be comprehensively validated and evaluated by simulations and real-world application examples of practical relevance. The usage of real data is directly related to kinematic MSSs and the related tasks of calibration and georeferencing. The latter especially with regard to complex innercity environments. In such challenging environments, the requirements for georeferencing under integrity aspects are of special importance. Therefore, the simultaneous use of independent and complementary information sources is applied in this thesis. This enables a reliable georeferencing solution to be achieved and a prompt notification to be issued in case of integrity violations.Note de contenu : 1- Introduction
2- Fundamentals of Recursive State-space Filtering
3- Methodological contributions
4- Kinematic Multi-sensor Systems and Their Efficient Calibration
5- Information-based Georeferencing
6- ConclusionsNuméro de notice : 17686 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geodäsie und Geoinformatik : Hanovre : 2020 DOI : sans En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-856.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98164 Learning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)PermalinkLow-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network / Yuxing Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)PermalinkPermalinkSmoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors / Paul Chauchat (2020)PermalinkKalman-filter-based undifferenced cycle slip estimation in real-time precise point positioning / Pan Li in GPS solutions, vol 23 n° 4 (October 2019)PermalinkSaliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)PermalinkAn analytic expression for the phase noise of the goldstein–werner filter / Scott Hensley in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkCombination of GRACE monthly gravity fields on the normal equation level / Ulrich Meyer in Journal of geodesy, vol 93 n° 9 (September 2019)PermalinkComparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey / Baris Suleymanoglu in Geodetski vestnik, vol 63 n° 3 (September - November 2019)PermalinkDecomposition of geodetic time series: A combined simulated annealing algorithm and Kalman filter approach / Feng Ming in Advances in space research, vol 64 n°5 (1 September 2019)Permalink