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Long-term prediction of polar motion using a combined SSA and ARMA model / Y. Shen in Journal of geodesy, vol 92 n° 3 (March 2018)  

Public [article]  inJournal of geodesy > vol 92 n° 3 (March 2018) . - pp 333 - 343 Titre : Long-term prediction of polar motion using a combined SSA and ARMA model Type de document : Article/Communication Auteurs : Y. Shen, Auteur ; Jinyun Guo, Auteur ; X. Liu, Auteur ; Qiaoli Kong, Auteur ; Linxi Guo, Auteur ; Li Wang, Auteur Année de publication : 2018 Article en page(s) : pp 333 - 343 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse de spectre singulier [Termes IGN] analyse en composantes principales [Termes IGN] modèle de simulation [Termes IGN] mouvement du pôle Mots-clés libres : modèle ARMA Résumé : (Auteur) To meet the need for real-time and high-accuracy predictions of polar motion (PM), the singular spectrum analysis (SSA) and the autoregressive moving average (ARMA) model are combined for short- and long-term PM prediction. According to the SSA results for PM and the SSA prediction algorithm, the principal components of PM were predicted by SSA, and the remaining components were predicted by the ARMA model. In applying this proposed method, multiple sets of PM predictions were made with lead times of two years, based on an IERS 08 C04 series. The observations and predictions of the principal components correlated well, and the SSA + ARMA model effectively predicted the PM. For 360-day lead time predictions, the root-mean-square errors (RMSEs) of PMx and PMy were 20.67 and 20.42 mas, respectively, which were less than the 24.46 and 24.78 mas predicted by IERS Bulletin A. The RMSEs of PMx and PMy in the 720-day lead time predictions were 28.61 and 27.95 mas, respectively. Numéro de notice : A2018-061 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-017-1065-3 Date de publication en ligne : 12/09/2017 En ligne : https://doi.org/10.1007/s00190-017-1065-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89397 [article]

Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features / Z. He in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5150 -5163 Titre : Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features Type de document : Article/Communication Auteurs : Z. He, Auteur ; Qiang Wang, Auteur ; Y. Shen, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5150 -5163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classificateur [Termes IGN] décomposition en fonctions orthogonales empiriques [Termes IGN] image hyperspectrale [Termes IGN] pouvoir de résolution géométrique [Termes IGN] pouvoir de résolution spectrale [Termes IGN] précision de la classification [Termes IGN] transformation en ondelettes Résumé : (Auteur) Recently, many researchers have attempted to exploit spectral-spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral-spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral-spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples. Numéro de notice : A2014-436 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287022 En ligne : https://doi.org/10.1109/TGRS.2013.2287022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73973 [article]

Exemplaires(1)

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065-2014081B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Efficient estimation of variance and covariance components : A case study for GPS stochastic model evaluation / B. Li in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 1 (January 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 1 Tome 1 (January 2011) . - pp 203 - 210 Titre : Efficient estimation of variance and covariance components : A case study for GPS stochastic model evaluation Type de document : Article/Communication Auteurs : B. Li, Auteur ; Y. Shen, Auteur ; L. Lou, Auteur Année de publication : 2011 Conférence : IGARSS 2009, International Geoscience And Remote Sensing Symposium 12/07/2009 17/07/2009 Le Cap Afrique du sud Proceedings IEEE Article en page(s) : pp 203 - 210 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] covariance [Termes IGN] matrice de covariance [Termes IGN] modèle stochastique [Termes IGN] positionnement par GPS [Termes IGN] variance Résumé : (Auteur) The variance and covariance component estimation (VCE) has been extensively investigated. However, in real application, the bottleneck problem is the huge computation burden, particularly when many variance and covariance components are involved for many heterogeneous observations. The objective of this paper is to develop a new method allowing the efficient estimation of variance and covariance components. The core of the new method is to construct an orthogonal complement matrix of the coefficient matrix in a Gauss-Markov model using only the coefficient matrix itself. Therefore, the constructed matrix and the computed discrepancies of measurements with each other, which are the essential inputs for the VCE, are invariant in the iterative procedure of computing the variance and covariance components. As a result, the computation efficiency is significantly improved. As a case study, we apply the new method to evaluate the GPS stochastic model with 15 variance and covariance components demonstrating its superior performance. Comparing with the traditional VCE method, the equivalent results are achievable, and the computation efficiency is improved by 34.2%. In the future, much more sensors will be available, and plentiful data can be acquired. Therefore, the new method will be very promising to efficiently estimate the variance and covariance components of the measurements from the different sensors and reasonably balance their contributions to the fused solution, benefiting the higher time-resolution solutions. Numéro de notice : A2011-049 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2054100 Date de publication en ligne : 12/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2054100 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30830 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011011A	RAB	Revue	Centre de documentation	En réserve L003	Disponible