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A continuous change tracker model for remote sensing time series reconstruction / Yangjian Zhang in Remote sensing, vol 14 n° 9 (May-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 9 (May-1 2022) . - n° 2280 Titre : A continuous change tracker model for remote sensing time series reconstruction Type de document : Article/Communication Auteurs : Yangjian Zhang, Auteur ; Li Wang, Auteur ; Yuanhuizi He, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de filtrage [Termes IGN] analyse harmonique [Termes IGN] compression d'image [Termes IGN] détection de changement [Termes IGN] extraction de traits caractéristiques [Termes IGN] Leaf Area Index [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] phénologie [Termes IGN] production primaire brute [Termes IGN] reconstruction d'image [Termes IGN] réflectance de surface [Termes IGN] série temporelle Résumé : (auteur) It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions. Numéro de notice : A2022-384 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14092280 Date de publication en ligne : 09/05/2022 En ligne : https://doi.org/10.3390/rs14092280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100662 [article]

Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde / Li Wang in Space weather, vol 19 n° 3 (March 2021)  

Public [article]  inSpace weather > vol 19 n° 3 (March 2021) . - n° e2020SW002605 Titre : Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde Type de document : Article/Communication Auteurs : Li Wang, Auteur ; Zhao Dongsheng ; Changyong He , Auteur ; et al., Auteur Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° e2020SW002605 Note générale : bibliographie The authors greatly appreciate the financial support from the National Natural Science Foundations of China (Grant No. 41730109, 41804013), the Natural Science Foundation of Jiangsu Province (Grant No. BK20200646, BK20200664), the Fundamental Re-search Funds for the Central Universi-ties (Grant No. 2020QN31, 2020QN30), the Project funded by China Postdoc-toral Science Foundation (Grant No. 2020M671645), the Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution (Grant No. KLSPWSEP-A06), A Project Funded by the Priority Academic Pro-gram Development of Jiangsu Higher Education Institutions (Surveying and Mapping). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] image Formosat/COSMIC [Termes IGN] modèle ionosphérique [Termes IGN] Perceptron multicouche [Termes IGN] réseau neuronal artificiel [Termes IGN] teneur totale en électrons [Termes IGN] variation saisonnière Résumé : (auteur) The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal-vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space-borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY-3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three-dimensional electron density model based on an artificial neural network, namely ANN-TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root-mean-square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN-TDD is 30%–60% higher than the IRI-2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN-TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI-2016 with the STORM option activated. Additionally, the ANN-TDD successfully reproduces the large-scale horizontal-vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC-2 mission. Furthermore, the ANN-TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid-latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved. Numéro de notice : A2021-504 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2020SW002605 Date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.1029/2020SW002605 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99369 [article]

Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 143 (September 2018) . - pp 85 - 96 Titre : Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning Type de document : Article/Communication Auteurs : Rui Zhang, Auteur ; Guangyun Li, Auteur ; Minglei Li, Auteur ; Li Wang, Auteur Année de publication : 2018 Article en page(s) : pp 85 - 96 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage profond [Termes IGN] détection du bâti [Termes IGN] fusion de données [Termes IGN] réseau neuronal convolutif [Termes IGN] scène 3D [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (Auteur) We address the issue of the semantic segmentation of large-scale 3D scenes by fusing 2D images and 3D point clouds. First, a Deeplab-Vgg16 based Large-Scale and High-Resolution model (DVLSHR) based on deep Visual Geometry Group (VGG16) is successfully created and fine-tuned by training seven deep convolutional neural networks with four benchmark datasets. On the val set in CityScapes, DVLSHR achieves a 74.98% mean Pixel Accuracy (mPA) and a 64.17% mean Intersection over Union (mIoU), and can be adapted to segment the captured images (image resolution 2832 ∗ 4256 pixels). Second, the preliminary segmentation results with 2D images are mapped to 3D point clouds according to the coordinate relationships between the images and the point clouds. Third, based on the mapping results, fine features of buildings are further extracted directly from the 3D point clouds. Our experiments show that the proposed fusion method can segment local and global features efficiently and effectively. Numéro de notice : A2018-356 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.022 Date de publication en ligne : 11/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90590 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018091	RAB	Livre	Centre de documentation	En réserve L003	Disponible
081-2018093	DEP-EXM	Livre	LASTIG	Dépôt en unité	Exclu du prêt
081-2018092	DEP-EAF	Livre	Nancy	Dépôt en unité	Exclu du prêt

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]

BeiDou belongs to China, and to the World / Li Wang in GPS world, vol 25 n° 12 (décembre 2014)  

Public [article]  inGPS world > vol 25 n° 12 (décembre 2014) . - pp. 30 - 31 Titre : BeiDou belongs to China, and to the World Type de document : Article/Communication Auteurs : Li Wang, Auteur Année de publication : 2014 Article en page(s) : pp. 30 - 31 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] constellation BeiDou [Termes IGN] géodésie spatiale [Termes IGN] positionnement par GNSS [Termes IGN] signal BeiDou Index. décimale : 30.61 Systèmes de Positionnement par Satellites du GNSS Résumé : (Auteur)[introduction] By adhering to the principles of independences, openess, compatibility and gradualness, China is steadly accelerating the construction and development of the BeiDou Navigation Satellite System. Numéro de notice : A2014-612 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : sans En ligne : http://gpsworld.com/directions-2015-beidou-belongs-to-china-and-to-the-world/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74881 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
067-2014121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An effective morphological index in automatic recognition of built-up area suitable for high spatial resolution images as ALOS and SPOT data / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)  

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