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Etendre la recherche sur niveau(x) vers le bas
POD of small LEO satellites based on precise real-time MADOCA and SBAS-aided PPP corrections / Amir Allahvirdi-Zadeh in GPS solutions, vol 25 n° 2 (April 2021)
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Titre : POD of small LEO satellites based on precise real-time MADOCA and SBAS-aided PPP corrections Type de document : Article/Communication Auteurs : Amir Allahvirdi-Zadeh, Auteur ; Kan Wang, Auteur ; Ahmed El-Mowafy, Auteur Année de publication : 2021 Article en page(s) : 14 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Techniques orbitales
[Termes descripteurs IGN] données GNSS
[Termes descripteurs IGN] horloge du satellite
[Termes descripteurs IGN] orbite basse
[Termes descripteurs IGN] orbitographie par GNSS
[Termes descripteurs IGN] positionnement ponctuel précis
[Termes descripteurs IGN] temps réelRésumé : (Auteur) For real-time precise orbit determination (POD) of low earth orbit (LEO) satellites, high-accuracy global navigation satellite system (GNSS) orbit and clock products are necessary in real time. Recently, the Japanese multi-GNSS advanced demonstration of orbit and clock analysis precise point positioning (PPP) service and the new generation of the Australian/New Zealand satellite-based augmentation system (SBAS)-aided PPP service provide free and precise GNSS products that are directly broadcast through the navigation and geostationary earth orbit satellites, respectively. With the high quality of both products shown in this study, a 3D accuracy of centimeters can be achieved in the post-processing mode for the reduced-dynamic orbits of small LEO satellites having a duty cycle down to 40% and at sub-dm to dm level for the kinematic orbits. The results show a promising future for high-accuracy real-time POD onboard LEO satellites benefiting from the precise free-of-charge PPP corrections broadcast by navigation systems or SBAS. Numéro de notice : A2021-091 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01078-8 date de publication en ligne : 11/01/2021 En ligne : https://doi.org/10.1007/s10291-020-01078-8 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96880
in GPS solutions > vol 25 n° 2 (April 2021) . - 14 p.[article]Precipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)
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Titre : Precipitable water vapor fusion based on a generalized regression neural network Type de document : Article/Communication Auteurs : Bao Zhang, Auteur ; Yibing Yao, Auteur Année de publication : 2021 Article en page(s) : n° 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes descripteurs IGN] Amérique du nord
[Termes descripteurs IGN] coefficient d'étalonnage
[Termes descripteurs IGN] coefficient de corrélation
[Termes descripteurs IGN] données GNSS
[Termes descripteurs IGN] données météorologiques
[Termes descripteurs IGN] erreur systématique
[Termes descripteurs IGN] fusion de données
[Termes descripteurs IGN] image Aqua-MODIS
[Termes descripteurs IGN] image Terra-MODIS
[Termes descripteurs IGN] précipitation
[Termes descripteurs IGN] prévision météorologique
[Termes descripteurs IGN] régression
[Termes descripteurs IGN] réseau neuronal artificiel
[Termes descripteurs IGN] vapeur d'eau
[Termes descripteurs IGN] variation temporelleRésumé : (auteur) Water vapor plays an important role in Earth’s weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used. Numéro de notice : A2021-259 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01482-z date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1007/s00190-021-01482-z Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97299
in Journal of geodesy > vol 95 n° 4 (April 2021) . - n° 36[article]Rotation-invariant feature learning in VHR optical remote sensing images via nested siamese structure with double center loss / Ruoqiao Jiang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
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Titre : Rotation-invariant feature learning in VHR optical remote sensing images via nested siamese structure with double center loss Type de document : Article/Communication Auteurs : Ruoqiao Jiang, Auteur ; Shaohui Mei, Auteur ; Mingyang Ma, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3326 - 3337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] détection d'objet
[Termes descripteurs IGN] données d'apprentissage
[Termes descripteurs IGN] échantillon
[Termes descripteurs IGN] extraction de traits caractéristiques
[Termes descripteurs IGN] image à très haute résolution
[Termes descripteurs IGN] invariant
[Termes descripteurs IGN] réseau neuronal siamois
[Termes descripteurs IGN] rotationRésumé : (auteur) Rotation-invariant features are of great importance for object detection and image classification in very-high-resolution (VHR) optical remote sensing images. Though multibranch convolutional neural network (mCNN) has been demonstrated to be very effective for rotation-invariant feature learning, how to effectively train such a network is still an open problem. In this article, a nested Siamese structure (NSS) is proposed for training the mCNN to learn effective rotation-invariant features, which consists of an inner Siamese structure to enhance intraclass cohesion and an outer Siamese structure to enlarge interclass margin. Moreover, a double center loss (DCL) function, in which training samples from the same class are mapped closer to each other while those from different classes are mapped far away to each other, is proposed to train the proposed NSS even with a small amount of training samples. Experimental results over three benchmark data sets demonstrate that the proposed NSS trained by DCL is very effective to encounter rotation varieties when learning features for image classification and outperforms several state-of-the-art rotation-invariant feature learning algorithms even when a small amount of training samples are available. Numéro de notice : A2021-286 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3021283 date de publication en ligne : 18/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3021283 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97395
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3326 - 3337[article]Scene classification of remotely sensed images via densely connected convolutional neural networks and an ensemble classifier / Qimin Cheng in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)
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Titre : Scene classification of remotely sensed images via densely connected convolutional neural networks and an ensemble classifier Type de document : Article/Communication Auteurs : Qimin Cheng, Auteur ; Yuan Xu, Auteur ; Peng Fu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 295-308 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] architecture de réseau
[Termes descripteurs IGN] classification par réseau neuronal convolutifRésumé : (Auteur) Deep learning techniques, especially convolutional neural networks, have boosted performance in analyzing and understanding remotely sensed images to a great extent. However, existing scene-classification methods generally neglect local and spatial information that is vital to scene classification of remotely sensed images. In this study, a method of scene classification for remotely sensed images based on pretrained densely connected convolutional neural networks combined with an ensemble classifier is proposed to tackle the under-utilization of local and spatial information for image classification. Specifically, we first exploit the pretrained DenseNet and fine-tuned it to release its potential in remote-sensing image feature representation. Second, a spatial-pyramid structure and an improved Fisher-vector coding strategy are leveraged to further strengthen representation capability and the robustness of the feature map captured from convolutional layers. Then we integrate an ensemble classifier in our network architecture considering that lower attention to feature descriptors. Extensive experiments are conducted, and the proposed method achieves superior performance on UC Merced, AID, and NWPU-RESISC45 data sets. Numéro de notice : A2021-334 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.3.295 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.3.295 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97533
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 295-308[article]Stop-and-move sequence expressions over semantic trajectories / Yenier Torres Izquierdo in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)
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Titre : Stop-and-move sequence expressions over semantic trajectories Type de document : Article/Communication Auteurs : Yenier Torres Izquierdo, Auteur ; Grettel Monteagudo Garcia, Auteur ; Marco A. Casanova, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 793 - 818 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] algorithme de recherche
[Termes descripteurs IGN] appariement sémantique
[Termes descripteurs IGN] exploration de données
[Termes descripteurs IGN] image Flickr
[Termes descripteurs IGN] information sémantique
[Termes descripteurs IGN] intelligence artificielle
[Termes descripteurs IGN] langage de requête
[Termes descripteurs IGN] RDF
[Termes descripteurs IGN] SPARQLRésumé : (auteur) Stop-and-move semantic trajectories are segmented trajectories where the stops and moves are semantically enriched with additional data. A query language for semantic trajectory datasets has to include selectors for stops or moves based on their enrichments and sequence expressions that define how to match the results of selectors with the sequence the semantic trajectory defines. This article addresses the problem of searching semantic trajectories, using stop-and-move sequence expressions. The article first proposes a formal framework to define semantic trajectories and introduces stop-and-move sequence expressions, with well-defined syntax and semantics, which act as an expressive query language for semantic trajectories. Then, it describes a concrete semantic trajectory model in RDF, defines SPARQL stop-and-move sequence expressions and discusses strategies to compile such expressions into SPARQL queries. Lastly, the article specifies user-friendly keyword search expressions over semantic trajectories based on the use of keywords to specify stop-and-move queries, and the adoption of terms with predefined semantics to compose sequence expressions. It then shows how to compile such keyword search expressions into SPARQL queries. Finally, it provides a proof-of-concept experiment over a semantic trajectory dataset constructed with user-generated content from Flickr, combined with Wikipedia data. Numéro de notice : A2021-270 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1793157 date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1793157 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97328
in International journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 793 - 818[article]The delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods / Akhtar Jamil in Geocarto international, vol 36 n° 7 ([01/04/2021])
PermalinkUnsupervised pansharpening based on self-attention mechanism / Ying Qu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
PermalinkVisual positioning in indoor environments using RGB-D images and improved vector of local aggregated descriptors / Longyu Zhang in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)
PermalinkAnti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 6 ([30/03/2021])
PermalinkUrban growth analysis and simulations using cellular automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan / Aigerim Ilyassova in Geocarto international, vol 36 n° 5 ([15/03/2021])
PermalinkAnalysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)
PermalinkAutomated registration of SfM‐MVS multitemporal datasets using terrestrial and oblique aerial images / Luigi Parente in Photogrammetric record, vol 36 n° 173 (March 2021)
PermalinkAutomating and utilising equal-distribution data classification / Gennady Andrienko in International journal of cartography, vol 7 n° 1 (March 2021)
PermalinkPermalinkDetection of subpixel targets on hyperspectral remote sensing imagery based on background endmember extraction / Xiaorui Song in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
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