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Impact of different sampling rates on precise point positioning performance using online processing service / Serdar Erol in Geo-spatial Information Science, vol 24 n° 2 (June 2021)  

Public [article]  inGeo-spatial Information Science > vol 24 n° 2 (June 2021) . - pp 302 - 312 Titre : Impact of different sampling rates on precise point positioning performance using online processing service Type de document : Article/Communication Auteurs : Serdar Erol, Auteur ; Reha Metin Alkan, Auteur ; I. Murat Ozulu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 302 - 312 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] données GNSS [Termes IGN] format RINEX [Termes IGN] instrumentation Trimble [Termes IGN] intervalle de confiance [Termes IGN] phase GNSS [Termes IGN] positionnement cinématique en temps réel [Termes IGN] positionnement ponctuel précis [Termes IGN] précision du positionnement [Termes IGN] rapport signal sur bruit [Termes IGN] réalité de terrain [Termes IGN] retard troposphérique zénithal [Termes IGN] taux d'échantillonnage [Termes IGN] trajet multiple [Termes IGN] Turquie Résumé : (auteur) In this study, the effect of different sampling rates (i.e. observation recording interval) on the Precise Point Positioning (PPP) solutions in terms of accuracy was investigated. For this purpose, a field test was carried out in Çorum province, Turkey, on 11 September 2019. Within this context, a Geodetic Point (GP) was established and precisely coordinated. A static GNSS measurement was occupied on the GP for about 4-hour time at 0.10 second (s)/10 Hz measurement intervals with the Trimble R10 geodetic grade GNSS receiver. The original observation file was converted to RINEX format and then decimated into the different data sampling rates as 0.2 s, 0.5 s, 1 s, 5 s, 10 s, 30 s, 60 s, and 120 s. All these RINEX observation files were submitted to the Canadian Spatial Reference System-Precise Point Positioning (CSRS-PPP) online processing service the day after the data collection date by choosing both static and kinematic processing options. In this way, PPP-derived static coordinates, and the kinematic coordinates of each measurement epoch were calculated. The PPP-derived coordinates obtained from each decimated sampling intervals were compared to known coordinates of the GP for northing, easting, 2D position, and height components. According to the static and kinematic processing results, high data sampling rates did not change the PPP solutions in terms of accuracy when compared to the results obtained using lower sampling rates. The results of this study imply that it was not necessary to collect GNSS data with high-rate intervals for many surveying projects requiring cm-level accuracy. Numéro de notice : A2021-558 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1842811 date de publication en ligne : 25/11/2020 En ligne : https://doi.org/10.1080/10095020.2020.1842811 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98111 [article]

Python software tools for GNSS interferometric reflectometry (GNSS-IR) / Angel Martín in GPS solutions, Vol 24 n° 4 (October 2020)  

Public [article]  inGPS solutions > Vol 24 n° 4 (October 2020) . - 7 p. Titre : Python software tools for GNSS interferometric reflectometry (GNSS-IR) Type de document : Article/Communication Auteurs : Angel Martín, Auteur ; Raquel Luján, Auteur ; Ana Belén Anquela, Auteur Année de publication : 2020 Article en page(s) : 7 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] antenne GNSS [Termes IGN] format RINEX [Termes IGN] humidité du sol [Termes IGN] Python (langage de programmation) [Termes IGN] rapport signal sur bruit [Termes IGN] réflectométrie par GNSS Résumé : (auteur) Global Navigation Satellite System (GNSS) interferometric reflectometry, also known as the GNSS-IR, uses data from geodetic-quality GNSS antennas to extract information about the environment surrounding the antenna. Soil moisture monitoring is one of the most important applications of the GNSS-IR technique. This manuscript presents the main ideas and implementation decisions needed to write the Python code for software tools that transform RINEX format observation and navigation files into an appropriate format for GNSS-IR (which includes the SNR observations and the azimuth and elevation of the satellites) and to determine the reflection height and the adjusted phase and amplitude values of the interferometric wave for each individual satellite track. The main goal of the manuscript is to share the software with the scientific community to introduce new users to the GNSS-IR technique. Numéro de notice : A2020-523 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01010-0 date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1007/s10291-020-01010-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95696 [article]

X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 12 - 23 Titre : X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data Type de document : Article/Communication Auteurs : Danfeng Hong, Auteur ; Naoto Yokoya, Auteur ; Gui-Song Sia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 12 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage profond [Termes IGN] apprentissage semi-dirigé [Termes IGN] bruit blanc [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] scène urbaine [Termes IGN] transmission de données Résumé : (auteur) This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods. Numéro de notice : A2020-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.014 date de publication en ligne : 11/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95770 [article]

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Performance of Galileo precise time and frequency transfer models using quad-frequency carrier phase observations / Pengfei Zhang in GPS solutions, vol 24 n° 2 (April 2020)  

Public [article]  inGPS solutions > vol 24 n° 2 (April 2020) Titre : Performance of Galileo precise time and frequency transfer models using quad-frequency carrier phase observations Type de document : Article/Communication Auteurs : Pengfei Zhang, Auteur ; Rui Tu, Auteur ; Yuping Gao, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] bruit atmosphérique [Termes IGN] code Galileo [Termes IGN] décalage d'horloge [Termes IGN] erreur systématique interfréquence d'horloge [Termes IGN] fréquence multiple [Termes IGN] modèle mathématique [Termes IGN] phase [Termes IGN] positionnement ponctuel précis [Termes IGN] signal BeiDou [Termes IGN] signal Galileo [Termes IGN] signal GLONASS [Termes IGN] signal GNSS [Termes IGN] signal GPS [Termes IGN] temps-fréquence [Termes IGN] transmission de données Résumé : (auteur) GNSSs, such as Galileo and modernized GPS, BeiDou and GLONASS systems, offer new potential and challenges in precise time and frequency transfer using multi-frequency observations. We focus on the performance of Galileo time and frequency transfer using the E1, E5a, E5b and E5 observations. Dual-frequency, triple-frequency and quad-frequency models for precise time and frequency transfer with different Galileo observations are proposed. Four time and transfer links between international time laboratories are used to assess the performances of different models in terms of time link noise level and frequency stability indicators. The average RMS values of the smoothed residuals of the clock difference series are 0.033 ns, 0.033 ns and 0.034 ns for the dual-frequency, triple-frequency and quad-frequency models with four time links, respectively. With respect to frequency stability, the average stability values at 15,360 s are 9.51 × 10−15, 9.46 × 10−15 and 9.37 × 10−15 for the dual-frequency, triple-frequency and quad-frequency models with four time links, respectively. Moreover, although biases among different models and receiver the inter-frequency exist, their characteristics are relatively stable. Generally, the dual-/triple-/quad-frequency models show similar performance for those time links, and the quad-frequency models can provide significant potential for switching among and unifying the three multi-frequency solutions, as well as further enhancing the redundancy and reliability compared to the current dual-frequency time transfer method. Numéro de notice : A2020-083 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0955-7 date de publication en ligne : 04/02/2020 En ligne : https://doi.org/10.1007/s10291-020-0955-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94652 [article]

What, where, and how to transfer in SAR target recognition based on deep CNNs / Zhongling Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2324 - 2336 Titre : What, where, and how to transfer in SAR target recognition based on deep CNNs Type de document : Article/Communication Auteurs : Zhongling Huang, Auteur ; Zongxu Pan, Auteur ; Bin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 2324 - 2336 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de cible [Termes IGN] données multisources [Termes IGN] étiquette [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] source de données [Termes IGN] télédétection spatiale [Termes IGN] transmission de données Résumé : (auteur) Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated data set in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in synthetic aperture radar (SAR) image interpretation. Transfer learning provides an effective way to solve this problem by borrowing knowledge from the source task to the target task. In optical remote sensing application, a prevalent mechanism is to fine-tune on an existing model pretrained with a large-scale natural image data set, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR applications because of the prominent discrepancy between SAR and optical images. In this article, we attempt to discuss three issues that are seldom studied before in detail: 1) what network and source tasks are better to transfer to SAR targets; 2) in which layer are transferred features more generic to SAR targets; and 3) how to transfer effectively to SAR targets recognition. Based on the analysis, a transitive transfer method via multisource data with domain adaptation is proposed in this article to decrease the discrepancy between the source data and SAR targets. Several experiments are conducted on OpenSARShip. The results indicate that the universal conclusions about transfer learning in natural images cannot be completely applied to SAR targets, and the analysis of what and where to transfer in SAR target recognition is helpful to decide how to transfer more effectively. Numéro de notice : A2020-195 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947634 date de publication en ligne : 20/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947634 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94863 [article]

Mise en place d'un nouveau protocole relatif à la mise à jour de données géographiques produites par les Directions du Département des Hauts-de-Seine dans le SIG départemental / Gabriel Dousseau (2020) 

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Subsidence is determined in the heart of the Central Valley using Post Processed Static and Precise Point Positioning techniques / Y. Facio in Journal of applied geodesy, vol 14 n° 1 (January 2020)  

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Buildings in GI: How to deal with building models in the GIS domain / Laura Knoth in Transactions in GIS, vol 23 n° 3 (June 2019)  

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WFS 3.0 dans les starting blocks / Anonyme in Géomatique expert, n° 128 (juin - juillet 2019)

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Géomatique webmapping en open source / David Collado (2019)

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Le travail invisible des données / Jérôme Denis (2018)  

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Des données à l'information / Florent Chavand (2017)

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The location swapping method for geomasking / Su Zhang in Cartography and Geographic Information Science, Vol 44 n° 1 (January 2017)  

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Fourier-based multi-scale representation and progressive transmission of cartographic curves on the internet / Pengcheng Liu in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)  

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The need to integrate BIM and geoinformation / Sisi Zlatanova in GIM international [en ligne], vol 30 n° 10 (October 2016)  

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