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Assessing the positioning performance of GNSS receivers under different geomagnetic storm conditions / Chao Yan in Survey review, vol 54 n° 384 (May 2022)  

Public [article]  inSurvey review > vol 54 n° 384 (May 2022) . - pp 254 - 262 Titre : Assessing the positioning performance of GNSS receivers under different geomagnetic storm conditions Type de document : Article/Communication Auteurs : Chao Yan, Auteur ; Qing Wang, Auteur ; Bo Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 254 - 262 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] analyse comparative [Termes IGN] glissement de cycle [Termes IGN] perturbation ionosphérique [Termes IGN] positionnement ponctuel précis [Termes IGN] récepteur GNSS [Termes IGN] signal GNSS [Termes IGN] tempête magnétique Résumé : (auteur) GNSS signals are affected when solar activity causes sudden variations in the density of the ionosphere. Few studies concentrate on positioning performance of IGS stations using different GNSS receivers under different geomagnetic storm conditions. This paper for the first time presents IF and UC PPP positioning performance of stations with different receivers during the quiet, moderate, intense, and super storms period. Firstly, a comprehensive investigation of geomagnetic storms effects on the occurrence of GPS cycle-slip and PPP positioning performance have been presented. Secondly, the influences of geomagnetic storms on the occurrence of cycle-slip and IF PPP positioning performance for stations using receivers provided by ‘JAVAD’, ‘LEICA’, and ‘TRIMBLE’ manufacturers have been comprehensively studied. Finally, this study investigates the geomagnetic storms effects on IF PPP positioning performance of stations using receiver types ‘JAVAD TRE_G3TH DELTA’, ‘JAVAD TRE_3 DELTA’, ‘LEICA GR25’, and ‘TRIMBLE NETR9’ by analysing observed data collected at mid-latitude region. Numéro de notice : A2022-356 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1924967 Date de publication en ligne : 13/05/2021 En ligne : https://doi.org/10.1080/00396265.2021.1924967 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100555 [article]

A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation / Qing Wang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 145 - 165 Titre : A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation Type de document : Article/Communication Auteurs : Qing Wang, Auteur ; Hua Sun, Auteur ; Ruopu Li, Auteur ; Guangxing Wang, Auteur Année de publication : 2019 Article en page(s) : pp 145 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] forêt [Termes IGN] géostatistique [Termes IGN] image Landsat-OLI [Termes IGN] image SPOT 5 [Termes IGN] Mongolie intérieure (Chine) [Termes IGN] occupation du sol [Termes IGN] précision de la classification [Termes IGN] utilisation du sol [Termes IGN] variogramme Résumé : (Auteur) Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m-dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. Numéro de notice : A2019-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.011 Date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92871 [article]

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Accounting for the differential inter-system bias (DISB) of code observation in GPS+BDS positioning / Xiang Cao in Journal of applied geodesy, vol 13 n° 1 (January 2019)  

Public [article]  inJournal of applied geodesy > vol 13 n° 1 (January 2019) . - pp 63 - 68 Titre : Accounting for the differential inter-system bias (DISB) of code observation in GPS+BDS positioning Type de document : Article/Communication Auteurs : Xiang Cao, Auteur ; Qing Wang, Auteur ; Chengfa Gao, Auteur ; Jie Zhang, Auteur Année de publication : 2019 Article en page(s) : pp 63 - 68 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] erreur systématique inter-systèmes [Termes IGN] mesurage de pseudo-distance [Termes IGN] positionnement différentiel [Termes IGN] positionnement par BeiDou [Termes IGN] positionnement par GPS [Termes IGN] temps réel Résumé : (Auteur) If the associated differential inter-system biases (DISBs) are priori known, only one common reference satellite is sufficient, which is called the inter-system model. The inter-system model can help to maximize the redundancy of the positioning model, and thus can improve the positioning performance, especially in harsh environment. However, in practice use not all receivers can be calibrated with DISBs in advance. In this paper, taking combined GPS and BDS pseudorange positioning as the example, we compare three positioning models and their positioning performance. One is traditional intra-system model, and the other two belongs to the inter-system models, i. e. the model with calibration of DISB and the model with real-time estimation of DISB parameter. Positioning performance using the three models is evaluated with simulated obstructed environments. It will be shown that besides the model with calibration of DISB, the model with real-time estimation of DISB parameter can also effectively improve positioning accuracy and reliability compared with the traditional intra-system model, especially for the severely obstructed environment with only a few satellites observed. When no more than 7 satellites visible, the positioning accuracies in each directions can be improved by no less than 15 %. The proposed model can be used alternatively when no priori DISB calibration is available. Numéro de notice : A2019-135 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0025 Date de publication en ligne : 30/08/2018 En ligne : https://doi.org/10.1515/jag-2018-0025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92465 [article]

Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) / Chang Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1837 - 1859 Titre : Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) Type de document : Article/Communication Auteurs : Chang Li, Auteur ; Sisi Zhao, Auteur ; Qing Wang, Auteur ; Wenzhong Shi, Auteur Année de publication : 2018 Article en page(s) : pp 1837 - 1859 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] autocorrélation spatiale [Termes IGN] incertitude des données [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle numérique de surface [Termes IGN] propagation d'erreur Résumé : (Auteur) In the field of digital terrain analysis (DTA), the principle and method of uncertainty in surface area calculation (SAC) have not been deeply developed and need to be further studied. This paper considers the uncertainty of data sources from the digital elevation model (DEM) and SAC in DTA to perform the following investigations: (a) truncation error (TE) modeling and analysis, (b) modeling and analysis of SAC propagation error (PE) by using Monte-Carlo simulation techniques and spatial autocorrelation error to simulate DEM uncertainty. The simulation experiments show that (a) without the introduction of the DEM error, higher DEM resolution and lower terrain complexity lead to smaller TE and absolute error (AE); (b) with the introduction of the DEM error, the DEM resolution and terrain complexity influence the AE and standard deviation (SD) of the SAC, but the trends by which the two values change may be not consistent; and (c) the spatial distribution of the introduced random error determines the size and degree of the deviation between the calculated result and the true value of the surface area. This study provides insights regarding the principle and method of uncertainty in SACs in geographic information science (GIScience) and provides guidance to quantify SAC uncertainty. Numéro de notice : A2018-305 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1469136 Date de publication en ligne : 04/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1469136 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90448 [article]

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