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A new ZTD model based on permanent ground-based GNSS-ZTD data / M. Ding in Survey review, vol 48 n° 351 (October 2016)  

Public [article]  inSurvey review > vol 48 n° 351 (October 2016) . - pp 385 - 391 Titre : A new ZTD model based on permanent ground-based GNSS-ZTD data Type de document : Article/Communication Auteurs : M. Ding, Auteur ; W. Hu, Auteur ; X. Jin, Auteur ; L. Yu, Auteur Année de publication : 2016 Article en page(s) : pp 385 - 391 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] correction troposphérique [Termes IGN] données GNSS [Termes IGN] réseau neuronal artificiel [Termes IGN] retard troposphérique zénithal [Termes IGN] Russie Résumé : (Auteur) Tropospheric delay has a major effect on the accuracy of navigation and positioning when using the Global Navigation Satellite System (GNSS). Zenith tropospheric delay (ZTD) modelling has been used to weaken the influence of the atmosphere. The work reported here focused on ZTD modelling based on real-time surface meteorological parameters, traditionally represented by the Saastamoinen model. However, Saastamoinen accuracy only reaches scale of centimetres, even to scale of centimetres when the water vapour is active, whereas the scale of ground-based GNSS-ZTD data (i.e. ZTD derived from ground GNSS data) is on the millimetre scale and is considered to be the ‘true’ value. An important direction in GNSS studies is how to make good use of ground-based GNSS-ZTD data to improve the accuracy of the Saastamoinen model. Authors studied the residuals in the Saastamoinen model using high-precision GNSS-ZTD data provided by the International GNSS Service (IGS) product and then carried out modelling based on a back propagation neural network. A new ZTD model (ISAAS) based on real-time surface meteorological parameters is proposed based on this method. The ISAAS model has good accuracy: its BIAS and root mean square error (RMSE) at the test area in Russia were -4.4 and 20.4 mm, respectively, which are lower than the results obtained using the Saastamoinen model (-10.4 and 23.3 mm, respectively). The ISAAS model can improve the ZTD prediction accuracy by more than 12.4% and therefore has important implications for precision engineering measurements in Russia. Numéro de notice : A2016-821 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1179/1752270615Y.0000000034 En ligne : https://doi.org/10.1179/1752270615Y.0000000034 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82636 [article]

Outlier detection by using fault detection and isolation techniques in geodetic networks / U.M. Durdag in Survey review, vol 48 n° 351 (October 2016)  

Public [article]  inSurvey review > vol 48 n° 351 (October 2016) . - pp 400 - 408 Titre : Outlier detection by using fault detection and isolation techniques in geodetic networks Type de document : Article/Communication Auteurs : U.M. Durdag, Auteur ; S. Hekimoglu, Auteur ; B. Erdogan, Auteur Année de publication : 2016 Article en page(s) : pp 400 - 408 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] coordonnées GNSS [Termes IGN] décomposition [Termes IGN] données GNSS [Termes IGN] méthode de Monte-Carlo [Termes IGN] système de référence géodésique [Termes IGN] valeur aberrante Résumé : (Auteur) Fault detection and isolation (FDI) techniques, which are called standard parity space approach (SPSA) and optimal parity vector approach (OPVA), have been presented in literature extensively for engineering sensor systems or sensor networks. This paper demonstrates the abilities of these approaches to detect and isolate outliers in geodetic networks. The ability to detect and isolate outliers has been measured by computing the mean success rate (MSR) for some given probability of significance levels. These approaches have been applied to a levelling network and a Global Navigation Satellite System (GNSS) network. Different matrix decomposition techniques have been used as an alternative way to the Potter algorithm, which is used in SPSA and OPVA. It has been proven that the abilities of FDI techniques, i.e. the MSRs of OPVA, increase with regard to the ones of SPSA in the levelling network and the GNSS network especially if the significance level α is chosen as 0.001 by using Monte–Carlo simulation. Numéro de notice : A2016-822 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1179/1752270615Y.0000000038 En ligne : https://doi.org/10.1179/1752270615Y.0000000038 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82638 [article]

A mixed weighted least squares and weighted total least squares adjustment method and its geodetic applications / Y. Zhou in Survey review, vol 48 n° 351 (October 2016)  

Public [article]  inSurvey review > vol 48 n° 351 (October 2016) . - pp 421 - 429 Titre : A mixed weighted least squares and weighted total least squares adjustment method and its geodetic applications Type de document : Article/Communication Auteurs : Y. Zhou, Auteur ; X. Fang, Auteur Année de publication : 2016 Article en page(s) : pp 421 - 429 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie [Termes IGN] compensation par moindres carrés [Termes IGN] méthode des moindres carrés [Termes IGN] pondération [Termes IGN] variable Résumé : (Auteur) A mixed weighted least squares (WLS) and weighted total least squares (WTLS) (mixed WLS–WTLS) method is presented for an errors-in-variables (EIV) model with some fixed columns in the design matrix. The numerical computational scheme and an approximate accuracy assessment method are also provided. It is extended from the mixed Least squares (LS)–Total least squares (TLS) method to deal with the case that the random columns are corrupted by heteroscedastic correlated noises. The mixed WLS–WTLS method can improve the computational efficiency compared with the existing WTLS methods without loss of accuracy, particularly when the fixed columns are far more than random ones. The Bursa transformation and parallel lines fitting examples are carried out to demonstrate the performance of the proposed algorithm. Since the mixed WLS–WTLS problem includes both the WLS and the WTLS problem, it will have a more wide range of applications. Numéro de notice : A2016-823 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1179/1752270615Y.0000000040 En ligne : https://doi.org/10.1179/1752270615Y.0000000040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82639 [article]

Distributed texture-based land cover classification algorithm using hidden Markov model for multispectral data / S. Jenicka in Survey review, vol 48 n° 351 (October 2016)  

Public [article]  inSurvey review > vol 48 n° 351 (October 2016) . - pp 430 - 437 Titre : Distributed texture-based land cover classification algorithm using hidden Markov model for multispectral data Type de document : Article/Communication Auteurs : S. Jenicka, Auteur ; A. Suruliandi, Auteur Année de publication : 2016 Article en page(s) : pp 430 - 437 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] image IRS-LISS [Termes IGN] modèle de Markov caché [Termes IGN] occupation du sol [Termes IGN] précision de la classification [Termes IGN] texture d'image Résumé : (Auteur) Land cover classification is a vital application area in the satellite image processing domain. Texture is a useful feature in land cover classification. In this paper, we propose a distributed texture-based land cover classification algorithm using Hidden Markov Model (HMM). Here, HMM is used for texture-based classification of remotely sensed images. Furthermore, to enhance the performance, data-intensive remotely sensed image is segmented and distributed into parallel sessions. Experiments were conducted on IRS P6 LISS-IV data, and the results were evaluated based on the confusion matrix, classification accuracy, and Kappa statistics. These results indicate that the proposed algorithm achieves a classification accuracy of 88.75%. Numéro de notice : A2016-824 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1179/1752270615Y.0000000041 En ligne : https://doi.org/10.1179/1752270615Y.0000000041 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82640 [article]