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Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
[article]
Titre : Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework Type de document : Article/Communication Auteurs : Peyman Azimpour, Auteur ; Tahereh Bahraini, Auteur ; Hadi Sadoghi Yazdi, Auteur Année de publication : 2021 Article en page(s) : pp 3266 - 3276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification bayesienne
[Termes IGN] classification floue
[Termes IGN] distribution de Gauss
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] filtrage du bruit
[Termes IGN] filtre de Gauss
[Termes IGN] image hyperspectrale
[Termes IGN] Matlab
[Termes IGN] processeur graphique
[Termes IGN] qualité des données
[Termes IGN] variableRésumé : (auteur) The hyperspectral-image (HSI) noise-reduction step is a very significant preprocessing phase of data-quality enhancement. It has been attracting immense research attention in the remote sensing and image processing domains. Many methods have been developed for HSI restoration, the goal of which is to remove noise from the whole HSI cube simultaneously without considering the spectral–spatial similarity. When a noise-removal algorithm is used globally to the entire data set, it would not eliminate all levels of noise, effectively. Furthermore, most of the existing methods remove independent and identically distributed (i.i.d.) Gaussian noise. The real scenarios are much more complicated than this assumption. The complexity created by natural noise that has a non-i.i.d. structure leads to inefficient methods containing underestimation and invalid performance. In this article, we calculated the spatial–spectral similarity criteria by defining a set of clustering-based latent variables (CLVs) in a Bayesian framework to improve the robustness. These criteria can be extracted using the clustering operators. Then, by applying the CLV to the variational Bayesian model, we investigated a new low-rank matrix factorization denoising approach based on the proposed clustering-based latent variable (CLV-LRMF) to remove noise with the non-i.i.d. mixture of Gaussian structures. Finally, we switched to the GPU for MATLAB implementation to reduce the runtime. The experimental results show that the performance has been improved by applying the proposed CLV and demonstrate the effectiveness of the proposed CLV-LRMF over other state-of-the-art methods. Numéro de notice : A2021-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939512 Date de publication en ligne : 24/03/2021 En ligne : https://doi.org/10.1109/TGRS.2019.2939512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97396
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3266 - 3276[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)
[article]
Titre : Impact of the third frequency GNSS pseudorange and carrier phase observations on rapid PPP convergences Type de document : Article/Communication Auteurs : Jiang Guo, Auteur ; Jianghui Geng, Auteur ; Chen Wang, Auteur Année de publication : 2021 Article en page(s) : 12 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] bruit (théorie du signal)
[Termes IGN] fréquence multiple
[Termes IGN] ligne de base
[Termes IGN] mesurage de pseudo-distance
[Termes IGN] modèle fonctionnel
[Termes IGN] modèle stochastique
[Termes IGN] phase
[Termes IGN] positionnement ponctuel précis
[Termes IGN] résolution d'ambiguïté
[Termes IGN] signal BeiDou
[Termes IGN] signal Galileo
[Termes IGN] signal GNSS
[Termes IGN] temps de convergenceRésumé : (Auteur) New GNSS signals have significantly augmented positioning service and promoted algorithmic innovations such as rapid PPP convergence. With the emerging of multifrequency signals, it becomes essential to thoroughly explore the contribution of third frequency pseudorange and carrier phase toward PPP. In this study, we research the role of the third frequency observations on accelerating PPP convergence, commencing from both stochastic and functional models. We first constructed the stochastic model depending on the observation noise and then introduced two uncombined functional models with respect to different inter-frequency bias (IFB) estimation strategies. The double-differenced residuals based on a zero baseline were used to evaluate the signal noises, which were 0.09, 0.07, 0.11, 0.01 and 0.09 m for Galileo E1/E5a/E5b/E5/E6 pseudorange and 0.24, 0.31 and 0.05 m for BeiDou B1/B2/B3. Besides, carrier phase observations E5a/E5/E6/B1I/B3I shared a comparable signal noise of 0.002 m, while the signal noises of E1/E5b/B2I were 0.003 m. Both BeiDou-2/Galileo and Galileo-only float PPP were implemented based on the dataset collected from 25 stations, spanning 30 days. Triple-frequency Galileo PPP achieved convergence successfully in 19.9 min if observations were weighted according to observation precision, showing a comparable performance of dual-frequency PPP. Meanwhile, the convergence time of triple-frequency float PPP was further shortened to 19.2 min when satellite pair IFBs were eliminated by estimating a second satellite clock. While the improvement of triple-frequency float PPP was marginal, triple-frequency PPP-AR using signals E1/E5a/E6 shortened the initialization time of the dual-frequency counterpart by 38%. Moreover, the performance of triple-frequency PPP-AR kept almost unchanged after we excluded the third frequency pseudorange observations. We thus suggest that the contribution of the third frequency to PPP mainly rests on ambiguity resolution, favored by the additional carrier phase observations. Numéro de notice : A2021-090 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01079-7 Date de publication en ligne : 10/01/2021 En ligne : https://doi.org/10.1007/s10291-020-01079-7 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96875
in GPS solutions > vol 25 n° 2 (April 2021) . - 12 p.[article]Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC4A / Pierre Bosser in Earth System Science Data, vol 13 n° 4 (April 2021)
[article]
Titre : Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC4A Type de document : Article/Communication Auteurs : Pierre Bosser , Auteur ; Olivier Bock , Auteur ; Cyrille Flamant, Auteur ; Sandrine Bony, Auteur ; Sabrina Speich, Auteur Année de publication : 2021 Projets : GEMMOC / Bosser, Pierre, VEGAN / Bock, Olivier, EUREC4A / Bock, Olivier Article en page(s) : pp 1499 - 1517 Note générale : bibliographie
projets GEMMOC and VEGAN du CNRS program LEFE/INSU
Both the raw GNSS measurements and the IWV estimates are available through the AERIS data center (https://en.aeris-data.fr/). The digital object identifiers (DOIs) for R/V Atalante IWV and raw datasets are https://doi.org/10.25326/71 (Bosser et al., 2020a) and https://doi.org/10.25326/74 (Bosser et al., 2020d), respectively. The DOIs for the R/V Maria S. Merian IWV and raw datasets are https://doi.org/10.25326/72 (Bosser et al., 2020b) and https://doi.org/10.25326/75 (Bosser et al., 2020e), respectively. The DOIs for the R/V Meteor IWV and raw datasets are https://doi.org/10.25326/73 (Bosser et al., 2020c) and https://doi.org/10.25326/76 (Bosser et al., 2020f), respectively.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse comparative
[Termes IGN] coordonnées GNSS
[Termes IGN] données météorologiques
[Termes IGN] erreur systématique
[Termes IGN] navire
[Termes IGN] station permanente
[Termes IGN] teneur intégrée en vapeur d'eauRésumé : (auteur) In the framework of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) campaign that took place in January and February 2020, integrated water vapour (IWV) contents were retrieved over the open Tropical Atlantic Ocean using Global Navigation Satellite Systems (GNSS) data acquired from three research vessels (R/Vs): R/V Atalante, R/V Maria S. Merian, and R/V Meteor. This paper describes the GNSS processing method and compares the GNSS IWV retrievals with IWV estimates from the European Center for Medium-range Weather Forecast (ECMWF) fifth ReAnalysis (ERA5), from the Moderate-Resolution Imaging Spectroradiometer (MODIS) infra-red products, and from terrestrial GNSS stations located along the tracks of the ships. The ship-borne GNSS IWVs retrievals from R/V Atalante and R/V Meteor compare well with ERA5, with small biases (−1.62 kg m−2 for R/V Atalante and +0.65 kg m−2 for R/V Meteor) and a root mean square (RMS) difference about 2.3 kg m−2. The results for the R/V Maria S. Merian are found to be of poorer quality, with RMS difference of 6 kg m−2 which are very likely due to the location of the GNSS antenna on this R/V prone to multipath effects. The comparisons with ground-based GNSS data confirm these results. The comparisons of all three R/V IWV retrievals with MODIS infrared product show large RMS differences of 5–7 kg m−2, reflecting the enhanced uncertainties of this satellite product in the tropics. These ship-borne IWV retrievals are intended to be used for the description and understanding of meteorological phenomena that occurred during the campaign, east of Barbados, Guyana and northern Brazil. Numéro de notice : A2021-064 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-13-1499-2021 En ligne : https://doi.org/10.5194/essd-13-1499-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96840
in Earth System Science Data > vol 13 n° 4 (April 2021) . - pp 1499 - 1517[article]Precipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)
[article]
Titre : Precipitable water vapor fusion based on a generalized regression neural network Type de document : Article/Communication Auteurs : Bao Zhang, Auteur ; Yibing Yao, Auteur Année de publication : 2021 Article en page(s) : n° 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] Amérique du nord
[Termes IGN] coefficient d'étalonnage
[Termes IGN] coefficient de corrélation
[Termes IGN] données GNSS
[Termes IGN] données météorologiques
[Termes IGN] erreur systématique
[Termes IGN] fusion de données
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] précipitation
[Termes IGN] prévision météorologique
[Termes IGN] régression
[Termes IGN] réseau neuronal artificiel
[Termes IGN] vapeur d'eau
[Termes IGN] variation temporelleRésumé : (auteur) Water vapor plays an important role in Earth’s weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used. Numéro de notice : A2021-259 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01482-z Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1007/s00190-021-01482-z Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97299
in Journal of geodesy > vol 95 n° 4 (April 2021) . - n° 36[article]Spectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines / Rogério Galante Negri in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
[article]
Titre : Spectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines Type de document : Article/Communication Auteurs : Rogério Galante Negri, Auteur ; Alejandro C. Frery, Auteur ; Wallace Casaca, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2863 - 2876 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse de sensibilité
[Termes IGN] classification non dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'ombre
[Termes IGN] détection de changement
[Termes IGN] détection des nuages
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image Landsat-OLI
[Termes IGN] image Sentinel-MSI
[Termes IGN] processus stochastique
[Termes IGN] zone homogèneRésumé : (auteur) Change detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objects such as clouds and shadows is a significant difficulty for many change-detection methods. We propose a new unsupervised change-detection framework to address those critical points. The notion of homogeneous regions is introduced together with a set of geometric operations and statistic-based criteria to characterize and distinguish formally the change and nonchange areas in a pair of remote sensing images. Moreover, a robust and statistically well-posed family of stochastic distances is also proposed, which allows comparing the probability distributions of different regions/objects in the images. These stochastic measures are then used to train a support-vector-machine-based approach in order to detect the change/nonchange areas. Three study cases using the images acquired with different sensors are given in order to compare the proposed method with other well-known unsupervised methods. Numéro de notice : A2021-282 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3009483 Date de publication en ligne : 24/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3009483 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97389
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 2863 - 2876[article]Analyse et consolidation des résultats sur les estimations de superficie du couvert forestier et de ses changements entre 2000 et 2016 en république du Congo / Suspense Averti Ifo in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkGeographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships / Sensen Wu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkGridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates / Franz Schug in Plos one, vol 16 n° 3 (March 2021)PermalinkIntegrity investigation of global ionospheric TEC maps for high-precision positioning / Jiaojiao Zhao in Journal of geodesy, vol 95 n° 3 (March 2021)PermalinkLandslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions / Amit Batar in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkModélisation des délais ionosphériques appliquée au traitement PPP-RTK centimétrique avec ambiguïtés entières de phase / Camille Parra in XYZ, n° 166 (mars 2021)PermalinkSpace-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach / Bisong Hu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkSusceptibilité aux glissements de terrain dans la ville d’Al Hoceima et sa périphérie : application de la méthode de la théorie de l’évidence / Taoufik Byou in Geomatica, vol 75 n° 1 (Mars 2021)PermalinkUrban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkAn anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)Permalink