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Deep learning-based local climate zone classification using Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lin Zhou in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

Public [article]  inGeo-spatial Information Science > vol 25 n° 3 (October 2022) . - pp 383 - 398 Titre : Deep learning-based local climate zone classification using Sentinel-1 SAR and Sentinel-2 multispectral imagery Type de document : Article/Communication Auteurs : Lin Zhou, Auteur ; Zhenfeng Shao, Auteur ; Shugen Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 383 - 398 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage profond [Termes IGN] carte climatique [Termes IGN] Chine [Termes IGN] filtre de déchatoiement [Termes IGN] ilot thermique urbain [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] température de l'air Résumé : (auteur) As a newly developed classification system, the LCZ scheme provides a research framework for Urban Heat Island (UHI) studies and standardizes the worldwide urban temperature observations. With the growing popularity of deep learning, deep learning-based approaches have shown great potential in LCZ mapping. Three major cities in China are selected as the study areas. In this study, we design a deep convolutional neural network architecture, named Residual combined Squeeze-and-Excitation and Non-local Network (RSNNet), that consists of the Squeeze-and-Excitation (SE) block and non-local block to classify LCZ using freely available Sentinel-1 SAR and Sentinel-2 multispectral imagery. Overall Accuracy (OA) of 0.9202, 0.9524 and 0.9004 for three selected cities are obtained by applying RSNNet and training data of individual city, and OA of 0.9328 is obtained by training RSNNet with data from all three cities. RSNNet outperforms other popular Convolutional Neural Networks (CNNs) in terms of LCZ mapping accuracy. We further design a series of experiments to investigate the effect of different characteristics of Sentinel-1 SAR data on the performance of RSNNet in LCZ mapping. The results suggest that the combination of SAR and multispectral data can improve the accuracy of LCZ classification. The proposed RSNNet achieves an OA of 0.9425 when integrating the three decomposed components with Sentinel-2 multispectral images, 2.44% higher than using Sentinel-2 images alone. Numéro de notice : A2022-723 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2022.2030654 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1080/10095020.2022.2030654 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101666 [article]

Deep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 9 (September 2022) . - pp 2103 - 2130 Titre : Deep image deblurring: A survey Type de document : Article/Communication Auteurs : Kaihao Zhang, Auteur ; Wenqi Ren, Auteur ; Wenhan Luo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2103 - 2130 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation d'image [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déconvolution [Termes IGN] estimation par noyau [Termes IGN] filtrage du bruit [Termes IGN] image floue [Termes IGN] qualité d'image [Termes IGN] réseau antagoniste génératif [Termes IGN] taxinomie [Termes IGN] vision par ordinateur Résumé : (auteur) Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review. We start by discussing common causes of image blur, introduce benchmark datasets and performance metrics, and summarize different problem formulations. Next, we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison. In addition, we discuss some domain-specific deblurring applications including face images, text, and stereo image pairs. We conclude by discussing key challenges and future research directions. Numéro de notice : A2022-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-022-01633-5 Date de publication en ligne : 25/06/2022 En ligne : https://doi.org/10.1007/s11263-022-01633-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101444 [article]

Orbit determination, clock estimation and performance evaluation of BDS-3 PPP-B2b service / Chengpan Tang in Journal of geodesy, vol 96 n° 9 (September 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 9 (September 2022) . - n° 60 Titre : Orbit determination, clock estimation and performance evaluation of BDS-3 PPP-B2b service Type de document : Article/Communication Auteurs : Chengpan Tang, Auteur ; Xiaogong Hu, Auteur ; Jinping Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] filtre de Kalman [Termes IGN] horloge du satellite [Termes IGN] orbitographie [Termes IGN] positionnement par BeiDou [Termes IGN] positionnement ponctuel précis [Termes IGN] temps réel Résumé : (auteur) This paper focuses on PPP-B2b, one of the featured services for BDS-3, which provides users around China with centimetre-level static positioning accuracy and decimetre-level kinematic positioning accuracy by broadcasting precise corrections for GPS/BDS-3 satellites. The GEO PPP-B2b signal broadcasted information types, including the PRN mask, orbit corrections, differential code bias corrections and clock corrections, are introduced, as well as a brief description of their usage. A new orbit determination strategy using regional L-band code/phase measurements and inter-satellite link measurements in combination and a real-time clock estimation strategy based on the Kalman filter for PPP-B2b precise correction generation are introduced in this contribution. Then, the accuracy of the orbit and clock corrections is assessed. The orbit user ranging error (URE) is 0.05 m for the BDS-3 MEO and GPS satellites. The orbit URE for BDS-3 IGSO satellites is worse, i.e., 0.15 m. The clock correction accuracy is 0.2 ns for BDS-3 and GPS satellites. Finally, the PPP-B2b performance is validated by both the static PPP process and simulated kinematic PPP process. BDS-3/GPS dual-system PPP offers faster convergence and better accuracy. The positioning accuracy achievable using PPP-B2b real-time products is at the same level as that using post-processed products. The RMS for BDS-3/GPS dual-system static positioning errors is less than 1.0 cm in east and north and about 3.0 cm in the up. The simulated kinematic positioning accuracy is better than 2.5 cm in the north, 3.5 cm in the east and 8.5 cm in the up directions after convergence. Numéro de notice : A2022-669 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01642-9 Date de publication en ligne : 29/08/2022 En ligne : https://doi.org/10.1007/s00190-022-01642-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101553 [article]

Multiscale assimilation of Sentinel and Landsat data for soil moisture and Leaf Area Index predictions using an ensemble-Kalman-filter-based assimilation approach in a heterogeneous ecosystem / Nicola Montaldo in Remote sensing, vol 14 n° 14 (July-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 14 (July-2 2022) . - n° 3458 Titre : Multiscale assimilation of Sentinel and Landsat data for soil moisture and Leaf Area Index predictions using an ensemble-Kalman-filter-based assimilation approach in a heterogeneous ecosystem Type de document : Article/Communication Auteurs : Nicola Montaldo, Auteur ; Andrea Gaspa, Auteur ; Roberto Corona, Auteur Année de publication : 2022 Article en page(s) : n° 3458 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] assimilation des données [Termes IGN] bassin méditerranéen [Termes IGN] écosystème [Termes IGN] filtre de Kalman [Termes IGN] humidité du sol [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Leaf Area Index [Termes IGN] modèle dynamique [Termes IGN] modèle hydrographique [Termes IGN] Sardaigne [Termes IGN] zone semi-aride Résumé : (auteur) Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiarid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. A multiscale assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic–land surface model is proposed. It is based on the ensemble Kalman filter (EnKF), and it is not limited to assimilating remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), including saturated hydraulic conductivity and grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil–water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture, grass, and tree LAI in a heterogeneous ecosystem in Sardinia for a 3-year period with contrasting hydrometeorological (dry vs. wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared with the calibrated (“true”) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors of less than 4% and 15%, respectively; such results were obtained even with extremely biased initial model conditions. Numéro de notice : A2022-574 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14143458 En ligne : https://doi.org/10.3390/rs14143458 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101293 [article]

An accurate train positioning method using tightly-coupled GPS + BDS PPP/IMU strategy / Wei Jiang in GPS solutions, vol 26 n° 3 (July 2022)  

Public [article]  inGPS solutions > vol 26 n° 3 (July 2022) . - n° 67 Titre : An accurate train positioning method using tightly-coupled GPS + BDS PPP/IMU strategy Type de document : Article/Communication Auteurs : Wei Jiang, Auteur ; Mengyang Liu, Auteur ; Baigen Cai, Auteur Année de publication : 2022 Article en page(s) : n° 67 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] ambiguïté entière [Termes IGN] Chine [Termes IGN] filtre de Kalman [Termes IGN] phase [Termes IGN] positionnement inertiel [Termes IGN] positionnement par BeiDou [Termes IGN] positionnement par GPS [Termes IGN] positionnement ponctuel précis [Termes IGN] signal GPS [Termes IGN] simple différence [Termes IGN] train Résumé : (auteur) A new GNSS/IMU tightly coupled positioning system is introduced to train positioning. To fulfil a train control system’s aim of reducing the need to install trackside equipment, the GNSS precise point positioning (PPP) method is applied in place of the conventional differential GNSS method. As the railway environment has the character of long operational mileage and complex GNSS measurement conditions, the GPS and BDS constellations are combined with measurement processing to improve the system’s continuity and stability. Ultra-rapid GNSS orbit and clock product is used for real-time PPP. The GNSS-PPP and IMU are tightly coupled using an Extended Kalman filter with single-differenced ionospheric-free GPS + BDS carrier phase and pseudorange observations. The carrier phase ambiguities are estimated as “float” values every epoch to reduce the impact of GNSS signal loss-of-lock and cycle slips. A train experiment was conducted on the Qinghai-Tibet Railway to evaluate system performance. The results show that the proposed system has a better performance than the conventional methods, including GPS + BDS PPP, LC GPS + BDS PPP/IMU and TC GPS PPP/IMU, with 52.1%, 49.4% and 52.1%, respectively. The tightly-coupled GPS + BDS PPP/IMU system under conditions of partly blocked GNSS coverage was evaluated to evaluate the system's continuity. It was confirmed that the proposed system had more stable positioning results and higher positioning accuracy. Numéro de notice : A2022-361 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01250-2 Date de publication en ligne : 08/04/2022 En ligne : https://doi.org/10.1007/s10291-022-01250-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100580 [article]

Lidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks / Aurélien Brun in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

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Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data / Lukas Winiwarter in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

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An algorithm to assist the robust filter for tightly coupled RTK/INS navigation system / Zun Niu in Remote sensing, vol 14 n° 10 (May-2 2022)  

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Adaptive Kalman filter for real-time precise orbit determination of low earth orbit satellites based on pseudorange and epoch-differenced carrier-phase measurements / Min Li in Remote sensing, vol 14 n° 9 (May-1 2022)  

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Data assimilation of growing stock volume using a sequence of remote sensing data from different sensors / Niels Lindgren in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)  

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Detection and mitigation of GNSS spoofing via the pseudorange difference between epochs in a multicorrelator receiver / Xiangyong Shang in GPS solutions, vol 26 n° 2 (April 2022)  

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An approach to extracting digital elevation model for undulating and hilly terrain using de-noised stereo images of Cartosat-1 sensor / Litesh Bopche in Applied geomatics, vol 14 n° 1 (March 2022)  

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A method of vision aided GNSS positioning using semantic information in complex urban environment / Rui Zhai in Remote sensing, vol 14 n° 4 (February-2 2022)  

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GCN-Denoiser: mesh denoising with graph convolutional networks / Yuefan Shen in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)  

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GNSS/INS Kalman filter integrity monitoring with uncertain time correlated error processes / Omar Garcia Crespillo (2022)  

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