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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)  

Public [article]  inJournal of applied geodesy > vol 15 n° 1 (January 2021) . - pp 61 - 73 Titre : Characteristics of seasonal variations and noises of the daily double-difference and PPP solutions Type de document : Article/Communication Auteurs : Kamil Maciuk, Auteur ; Inese Vārna, Auteur ; Chang Xu, Auteur Année de publication : 2021 Article en page(s) : pp 61 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] bruit rose [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] Lettonie [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] station de référence [Termes descripteurs IGN] variance de Hadamard [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] vitesse [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) Long term GNSS observations provided by networks of the continuously operating reference stations (CORS) allow for determination of the global and local tectonic plate movements and seasonal variations. In recent years, PPP (Precise Point Positioning) technique has become increasingly popular and most likely in the future will replace relative positioning with CORS stations. In this paper, we discuss the difference of the velocity and seasonal component estimates of 25 Latvian CORS stations on the basis of daily PPP solutions from the Nevada Geodetic Laboratory and double-difference solutions from the Institute of Geodesy and Geoinformatics of the University of Latvia. Time series of each coordinate component for 9-year time period were determined by the usage of the Tsview software and seasonal existence of linear, annual, semi-annual factors and their uncertainties were determined. Breaks (e. g., antenna and receiver changes) were also taken into account. We then assessed the noise characteristics of these time series with the use of overlapping Hadamard variance (OHVAR). The result shows that OHVAR is computationally cheap, and the dominating power-law noise, including flicker and random walk. However Hadamard deviation of the PPP and double-difference solutions scatters differently for a whole year averaging time due to the different GNSS data strategies. Numéro de notice : A2021-045 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0042 date de publication en ligne : 08/12/2020 En ligne : https://doi.org/10.1515/jag-2020-0042 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96772 [article]

X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 12 - 23 Titre : X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data Type de document : Article/Communication Auteurs : Danfeng Hong, Auteur ; Naoto Yokoya, Auteur ; Gui-Song Sia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 12 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] apprentissage semi-dirigé [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] transmission de données Résumé : (auteur) This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods. Numéro de notice : A2020-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.014 date de publication en ligne : 11/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95770 [article]

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Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors / Boris Kargoll in Journal of geodesy, vol 94 n° 5 (May 2020)  

Public [article]  inJournal of geodesy > vol 94 n° 5 (May 2020) Titre : Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors Type de document : Article/Communication Auteurs : Boris Kargoll, Auteur ; Gaël Kermarrec, Auteur ; Hamza Alkhatib, Auteur ; Johannes Korte, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] algorithme espérance-maximisation [Termes descripteurs IGN] analyse vectorielle [Termes descripteurs IGN] autorégression [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] corrélation croisée normalisée [Termes descripteurs IGN] erreur aléatoire [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] station GPS [Termes descripteurs IGN] valeur aberrante Résumé : (auteur) The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). Numéro de notice : A2020-291 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01376-6 date de publication en ligne : 10/05/2020 En ligne : https://doi.org/10.1007/s00190-020-01376-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95120 [article]

Spectral Interference of Heavy Metal Contamination on Spectral Signals of Moisture Content for Heavy Metal Contaminated Soils / Haein Shin in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2266 - 2275 Titre : Spectral Interference of Heavy Metal Contamination on Spectral Signals of Moisture Content for Heavy Metal Contaminated Soils Type de document : Article/Communication Auteurs : Haein Shin, Auteur ; Jaehyung Yu, Auteur ; Lei Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2266 - 2275 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] arsenic [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] contamination [Termes descripteurs IGN] cuivre [Termes descripteurs IGN] dégradation du signal [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] interférence [Termes descripteurs IGN] métal lourd [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] plomb [Termes descripteurs IGN] pollution des sols [Termes descripteurs IGN] signature spectrale Résumé : (auteur) This article examined the spectral interference by heavy metal on the spectral signal of moisture content of heavy metal contaminated soils. Soil samples were collected from an abandoned mine area, and the chemical analysis revealed extremely high contamination amount of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb). The mineralogical analysis showed that the spectral signature of the heavy metal contaminated soils was manifested by secondary minerals. Water content suppressed the spectral reflectance of the soil samples but increased the absorption depths. Although a regression model can predict moisture content using the magnitude of the water absorption feature, the accuracy was much lower when the heavy metal concentration was extremely high. It indicates that geochemical reactions between the heavy metal cation and iron oxide/clay minerals may have affected the spectral responses of the contaminated soils at the water absorption bands. Our model also showed that there was a shift of the absorption features of moisture content if the heavy metal contamination level went up. Unlike normal soils, the absorption features of clay minerals and ferric iron were not able to accurately predict moisture in highly contaminated soils. Given the fact that the spectral bands selected in this article were associated with water absorption, the findings from this article may only be useful to a drone-based low-altitude remote sensing of soil moisture content. Numéro de notice : A2020-193 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946297 date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2946297 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94860 [article]

Poststack seismic data denoising based on 3-D convolutional neural network / Dawei Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1598 - 1629 Titre : Poststack seismic data denoising based on 3-D convolutional neural network Type de document : Article/Communication Auteurs : Dawei Liu, Auteur ; Dawei Liu, Auteur ; Xiaokai Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1598 - 1629 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] filtre de Gauss [Termes descripteurs IGN] post-stratification de données [Termes descripteurs IGN] séisme [Termes descripteurs IGN] sismologie Résumé : (Auteur) Deep learning has been successfully applied to image denoising. In this study, we take one step forward by using deep learning to suppress random noise in poststack seismic data from the aspects of network architecture and training samples. On the one hand, poststack seismic data denoising mainly aims at 3-D seismic data. We designed an end-to-end 3-D denoising convolutional neural network (3-D-DnCNN) that takes raw 3-D cubes as input in order to better extract the features of the 3-D spatial structure of poststack seismic data. On the other hand, denoising images with deep learning require noisy–clean sample pairs for training. In the field of seismic data processing, researchers usually try their best to suppress noise by using complex processes that combine different methods, but clean labels of seismic data are not available. In addition, building training samples in field seismic data has become an interesting but challenging problem. Therefore, we propose a training sample selection method that contains a complex workflow to produce comparatively ideal training samples. Experiments in this study demonstrate that deep learning can directly learn the ability to denoise field seismic data from selected samples. Although the building of the training samples may occur through a complex process, the experimental results of synthetic seismic data and field seismic data show that the 3-D-DnCNN has learned the ability to suppress the Gaussian noise and super-Gaussian noise from different training samples. Moreover, the 3-D-DnCNN network has better denoising performance toward arc-like imaging noise. In addition, we adopt residual learning and batch normalization in order to accelerate the training speed. After network training is satisfactorily completed, its processing efficiency can be significantly higher than that of conventional denoising methods. Numéro de notice : A2020-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947149 date de publication en ligne : 06/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947149 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94661 [article]

Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering / Liyuan Ma in European journal of remote sensing, vol 53 n°1 (2020)  

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Modelling of the timeseries of GNSS coordinates and their interaction with average magnitude earthquakes / Sanja Tucikesic in Geodetski vestnik, Vol 63 n° 4 (December 2019)  

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An 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)  

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Decomposition 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)  

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Investigation of the noise properties at low frequencies in long GNSS time series / Xiaoxing He in Journal of geodesy, vol 93 n° 9 (September 2019)  

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On the application of Monte Carlo singular spectrum analysis to GPS position time series / Seyed Mohsen Khazraei in Journal of geodesy, vol 93 n° 9 (September 2019)  

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Influence of stochastic modeling for inter-system biases on multi-GNSS undifferenced and uncombined precise point positioning / Feng Zhou in GPS solutions, vol 23 n° 3 (July 2019)  

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Signaux et systèmes / André Quinquis (2019)

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Bruit de scintillation dans les séries temporelles de positions GNSS / Paul Rebischung (2018) 

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Characterizing noise in daily GPS position time series with overlapping Hadamard variance and maximum likelihood estimation / Chang Xu in Survey review, vol 49 n° 355 (October 2017)  

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