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SAR speckle removal using hybrid frequency modulations / Shuaiqi Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3956 - 3966 Titre : SAR speckle removal using hybrid frequency modulations Type de document : Article/Communication Auteurs : Shuaiqi Liu, Auteur ; Lele Gao, Auteur ; Yu Lei, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3956 - 3966 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] artefact [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] filtrage du bruit [Termes IGN] filtre de déchatoiement [Termes IGN] image radar moirée [Termes IGN] modulation de fréquence Résumé : (auteur) Synthetic aperture radar (SAR) images often interfere with speckle artifacts that have a great impact on subsequent processing and analysis operations. To remove speckle artifacts, this article introduces a hybrid denoising approach by using a convolutional neural network (CNN) and consistent cycle spinning (CCS) in the nonsubsample shearlet transform (NSST) domain. First, we apply NSST to a noisy SAR image to gain low- and high-frequency coefficients. Second, we adopt a learned deep CNN model to eliminate the speckle noise in the low-frequency coefficients, which retains more contour information. Third, we employ CCS to enhance the high-frequency coefficients, which preserves more details of the original SAR image. Finally, we obtain the denoised image by using inverse NSST applied to the denoised coefficients. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only achieves better speckle removal performance but also maintains more detailed information retention. Numéro de notice : A2021-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3014130 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3014130 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97688 [article]

Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks / Yu Lei in Artificial satellites, vol 51 n° 4 (December 2016)  

Public [article]  inArtificial satellites > vol 51 n° 4 (December 2016) . - pp 149 – 161 Titre : Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks Type de document : Article/Communication Auteurs : Yu Lei, Auteur ; Danning Zhao, Auteur ; Hongbing Cai, Auteur Année de publication : 2016 Article en page(s) : pp 149 – 161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] filtre passe-bas [Termes IGN] fonction de base radiale [Termes IGN] mouvement du pôle [Termes IGN] oscillation [Termes IGN] prévision à court terme [Termes IGN] réseau neuronal artificiel [Termes IGN] terme de Chandler Résumé : (auteur) It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques. Numéro de notice : A2016-977 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/arsa-2016-0013 En ligne : https://doi.org/10.1515/arsa-2016-0013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83688 [article]