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Polarimetric SAR calibration and residual error estimation when corner reflectors are unavailable / Lei Shi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 4454 - 4471 Titre : Polarimetric SAR calibration and residual error estimation when corner reflectors are unavailable Type de document : Article/Communication Auteurs : Lei Shi, Auteur ; Pingxiang Li, Auteur ; Jie Yang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4454 - 4471 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] bruit (théorie du signal) [Termes descripteurs IGN] coin réflecteur [Termes descripteurs IGN] dégradation du signal [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] étalonnage [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] interruption du signal [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] polarisation croisée [Termes descripteurs IGN] rétrodiffusion de Bragg Résumé : (auteur) In this article, we propose a polarimetric calibration (PolCal) algorithm to estimate the system crosstalk, cross-polarization (x-pol), and co-polarization (co-pol) channel imbalance (CI) when ground corner reflectors (CRs) are unavailable. The current PolCal process requires at least one trihedral CR to determine the co-pol CI. However, the deployment of ground CRs is costly and may even be impossible in some areas. To calibrate a polarimetric image without CRs, our proposed method automatically extracts the volume-dominated and Bragg-like pixels as a reference to estimate the crosstalk, x-pol, and co-pol CI values. Then, a first-order polynomial model is exploited to fit the co-pol CI to further improve calibration accuracy. In the experimental section, we demonstrate the effectiveness of our proposed method with data from two of China’s newly developed very high-resolution systems. The experiments confirmed that the proposed workflow can be considered as a feasible calibration scheme when the ground deployment of CRs is impossible, and it is also an effective analysis tool for the assessment of calibrated products. Numéro de notice : A2020-286 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2964732 date de publication en ligne : 20/01/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2964732 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95109 [article]

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 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]

Performance of Galileo precise time and frequency transfer models using quad-frequency carrier phase observations / Pengfei Zhang in GPS solutions, vol 24 n° 2 (April 2020)  

Public [article]  inGPS solutions > vol 24 n° 2 (April 2020) Titre : Performance of Galileo precise time and frequency transfer models using quad-frequency carrier phase observations Type de document : Article/Communication Auteurs : Pengfei Zhang, Auteur ; Rui Tu, Auteur ; Yuping Gao, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] bruit atmosphérique [Termes descripteurs IGN] code Galileo [Termes descripteurs IGN] décalage d'horloge [Termes descripteurs IGN] erreur systématique interfréquence d'horloge [Termes descripteurs IGN] fréquence multiple [Termes descripteurs IGN] modèle mathématique [Termes descripteurs IGN] phase [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] signal BeiDou [Termes descripteurs IGN] signal Galileo [Termes descripteurs IGN] signal GLONASS [Termes descripteurs IGN] signal GNSS [Termes descripteurs IGN] signal GPS [Termes descripteurs IGN] temps-fréquence [Termes descripteurs IGN] transmission de données Résumé : (auteur) GNSSs, such as Galileo and modernized GPS, BeiDou and GLONASS systems, offer new potential and challenges in precise time and frequency transfer using multi-frequency observations. We focus on the performance of Galileo time and frequency transfer using the E1, E5a, E5b and E5 observations. Dual-frequency, triple-frequency and quad-frequency models for precise time and frequency transfer with different Galileo observations are proposed. Four time and transfer links between international time laboratories are used to assess the performances of different models in terms of time link noise level and frequency stability indicators. The average RMS values of the smoothed residuals of the clock difference series are 0.033 ns, 0.033 ns and 0.034 ns for the dual-frequency, triple-frequency and quad-frequency models with four time links, respectively. With respect to frequency stability, the average stability values at 15,360 s are 9.51 × 10−15, 9.46 × 10−15 and 9.37 × 10−15 for the dual-frequency, triple-frequency and quad-frequency models with four time links, respectively. Moreover, although biases among different models and receiver the inter-frequency exist, their characteristics are relatively stable. Generally, the dual-/triple-/quad-frequency models show similar performance for those time links, and the quad-frequency models can provide significant potential for switching among and unifying the three multi-frequency solutions, as well as further enhancing the redundancy and reliability compared to the current dual-frequency time transfer method. Numéro de notice : A2020-083 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0955-7 date de publication en ligne : 04/02/2020 En ligne : https://doi.org/10.1007/s10291-020-0955-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94652 [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 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 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 (Décember 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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Assessment of along-normal uncertainties for application to terrestrial laser scanning surveys of engineering structures / Tarvo Mill in Survey review, vol 51 n° 364 (January 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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