Descripteur
Documents disponibles dans cette catégorie (259)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)
[article]
Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] biomasse aérienne
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation bayesienne
[Termes IGN] forme d'onde
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495
in Remote sensing of environment > vol 268 (January 2022) . - n* 112760[article]Python software to transform GPS SNR wave phases to volumetric water content / Angel Martín in GPS solutions, vol 26 n° 1 (January 2022)
[article]
Titre : Python software to transform GPS SNR wave phases to volumetric water content Type de document : Article/Communication Auteurs : Angel Martín, Auteur ; Ana Belén Anquela, Auteur ; Sara Ibáñez, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] humidité du sol
[Termes IGN] phase
[Termes IGN] Python (langage de programmation)
[Termes IGN] rapport signal sur bruit
[Termes IGN] réflectométrie par GNSS
[Termes IGN] signal GPS
[Termes IGN] teneur en vapeur d'eauRésumé : (auteur) The global navigation satellite system interferometric reflectometry is often used to extract information about the environment surrounding the antenna. One of the most important applications is soil moisture monitoring. This manuscript presents the main ideas and implementation decisions needed to write the Python code to transform the derived phase of the interferometric GPS waves, obtained from signal-to-noise ratio data continuously observed during a period of several weeks (or months), to volumetric water content. The main goal of the manuscript is to share the software with the scientific community to help users in the GPS-IR computation. Numéro de notice : A2022-004 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01190-3 Date de publication en ligne : 27/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01190-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98919
in GPS solutions > vol 26 n° 1 (January 2022) . - n° 7[article]Le radar révèle des montagnes cachées / Laurent Polidori in Géomètre, n° 2198 (janvier 2022)
[article]
Titre : Le radar révèle des montagnes cachées Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2022 Article en page(s) : pp 17 - 17 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] altimétrie satellitaire par radar
[Termes IGN] Biomass
[Termes IGN] longueur d'onde
[Termes IGN] représentation du relief
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] traitement d'image radarRésumé : (Auteur) Un radar embarqué sur satellite peut voir des reliefs cachés par l’eau, la forêt, le sable ou la glace. Numéro de notice : A2022-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/01/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99401
in Géomètre > n° 2198 (janvier 2022) . - pp 17 - 17[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 063-2022011 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Robust GNSS phase tracking using variational bayesian inference Titre original : Méthodes de poursuite robuste de phase pour signaux GNSS basées sur l’inférence bayésienne variationnelle Type de document : Thèse/HDR Auteurs : Fabio Fabozzi, Auteur ; Stéphanie Bidon, Auteur Editeur : Toulouse : Université de Toulouse Année de publication : 2022 Importance : 173 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, délivré par l'Institut Supérieur de l’Aéronautique et de l’Espace, Spécialité Signal, Image, acoustique et optimisationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] données GNSS
[Termes IGN] filtrage bayésien
[Termes IGN] filtre de Kalman
[Termes IGN] glissement de cycle
[Termes IGN] inférence statistique
[Termes IGN] méthode robuste
[Termes IGN] phase
[Termes IGN] rapport signal sur bruit
[Termes IGN] récepteur GNSS
[Termes IGN] signal GNSSIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In this Ph.D. thesis, we are interested in robust carrier-phase estimation by using Variational Bayesian filtering. Carrier-phase measurement has become a fundamental task in many various engineering applications such as precise point positioning in GNSS (Global Navigation Satellite System). Unfortunately, phase measurements obtained by traditional phase tracking techniques may be strongly affected by the presence of ambiguous phase jumps, known as cycle slips. The latter may strongly impact the performance of the considered phase tracking algorithm leading to, in the worst case, a permanent loss of lock (drop-lock) of the signal. A re-acquistion process is then necessary which afflicts the tracking performance. Therefore, to address this problem, we propose a robust nonlinear filter for carrier-phase tracking based on Restricted Variational Bayes inference. This methodology gives us a closed-form and easy-to-implement expression of the estimator. First, the algorithm is developed only for slow phase dynamics (i.e., first-order loop), then, its order is augmented by estimating a state vector formed by the carrier-phase and its derivatives. The performance of the proposed algorithm is compared with that of conventional techniques such as DPLL (Digital Phase Lock Loop) and KF (Kalman Filter)-based DPLL in terms of precision of estimation (root-mean-square error) and cycle slipping occurrence (mean-time-to-first-slip and cycle slip rate). The comparison is firstly conducted using synthetic data, and then, real GNSS data into a GNSS software-defined-radio receiver. Results show that the proposed method outperforms the conventional linear filters, when the signal-to-noise ratio is low. Note de contenu : Introduction
1- Introduction to GNSS
2- DPLL and robust phase tracking techniques
3- RVB algorithm in case of slow dynamics
4- RVB algorithm in case of high-order dynamics
5- RVB algorithm using real GNSS data
ConclusionNuméro de notice : 15268 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse française Note de thèse : thèse de Doctorat : Signal, Image, acoustique et optimisation : Toulouse : 2022 Organisme de stage : ISAE-ONERA SCANR DOI : sans En ligne : https://depozit.isae.fr/theses/2022/2022_Fabozzi_Fabio.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100684 A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms / Ibrahim Fayad in Remote sensing of environment, vol 265 (November 2021)
[article]
Titre : A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms Type de document : Article/Communication Auteurs : Ibrahim Fayad, Auteur ; Dino Lenco, Auteur ; Nicolas Baghdadi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112652 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Brésil
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Eucalyptus (genre)
[Termes IGN] forme d'onde
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] hauteur des arbres
[Termes IGN] modèle de croissance végétale
[Termes IGN] semis de points
[Termes IGN] volume en boisRésumé : (auteur) Full waveform (FW) LiDAR systems have proven their effectiveness to map forest biophysical variables in the last two decades, owing to their ability of measuring, with high accuracy, forest vertical structures. The Global Ecosystem Dynamics Investigation (GEDI) system on board the International Space Station (ISS) is the latest FW spaceborne LiDAR instrument for the continuous observation of Earth's forests. FW systems rely on very sophisticated pre-processing steps to generate a priori metrics in order to leverage their capabilities for the accurate estimation of the aforementioned forest characteristics. The ever-expanding volume of acquired GEDI data, which to date comprises more than 25 billion acquired unfiltered shots, and along with the pre-processed data, amounting to more than 90 TB of data, raises new challenges in terms of adapted preprocessing methods for the suitable exploitation of such a huge and complex amount of LiDAR data. To overcome the issues related to the generation of relevant metrics from GEDI data, we propose a new metric-free approach to estimate canopy dominant heights (Hdom) and wood volume (V) of Eucalyptus plantations over five different regions in Brazil. To avoid metric computation, we leverage deep learning techniques and, more in detail, convolutional neural networks with the aim to analyze the GEDI Level 1B geolocated waveforms. Performance comparisons were conducted between four convolutional neural network (CNN) variants using GEDI waveform data (either untouched, or subsetted) and a metric based Random Forest regressor (RF). Additionally, we tested if our framework can improve the generalization of the models to different distant regions. First, the models were trained using data from all the study regions. Cross validated results showed that the CNN based models compared well against their RF counterpart for both Hdom and V. The RMSE on the estimation of Hdom from the CNN based models varied between 1.54 and 1.94 m with a coefficient of determination (R2) between 0.86 and 0.91, while the RF model produced an accuracy on Hdom estimates of 1.45 m (R2 = 0.92). For V, CNN based estimations ranged from 27.76 to 33.33 m3.ha−1 (R2 between 0.82 and 0.88), while for RF, the RMSE was 27.61 m3.ha−1 (R2 = 0.88). Next, model generalization was assessed by means of a spatial transfer experiment. For Hdom, both the CNN and RF approaches showed similar performances to a global model, however, the CNN based approach showed higher variability on the estimation accuracy, and the variability was related to the forest structure between the trained and tested data (similar tree heights yield better accuracies). For the estimation of V, considering both approaches, the accuracy was dependent on the allometric relationship between Hdom and V in the training and testing regions while lower accuracies on V were obtained when the testing and training regions exhibited a different allometric relationship. Numéro de notice : A2021-869 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112652 Date de publication en ligne : 31/08/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112652 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99118
in Remote sensing of environment > vol 265 (November 2021) . - n° 112652[article]Diffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression / Forrest Corcoran in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkFootprint size design of large-footprint full-waveform LiDAR for forest and topography applications: A theoretical study / Xuebo Yang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)PermalinkIonospheric tomographic common clock model of undifferenced uncombined GNSS measurements / German Olivares-Pulido in Journal of geodesy, vol 95 n° 11 (November 2021)PermalinkReal-time GNSS precise point positioning using improved robust adaptive Kalman filter / Abdelsatar Elmezayen in Survey review, Vol 53 n° 381 (November 2021)PermalinkVisualization of GNSS multipath effects and its potential application in IGS data processing / Weiming Tang in Journal of geodesy, vol 95 n° 9 (September 2021)PermalinkComparison of polar ionospheric behavior at Arctic and Antarctic regions for improved satellite-based positioning / Arun Kumar Singh in Journal of applied geodesy, vol 15 n° 3 (July 2021)PermalinkGeometric calibration of satellite laser altimeters based on waveform matching / Shaoning Li in Photogrammetric record, vol 36 n° 174 (June 2021)PermalinkGNSS-based statistical analysis of ionospheric anomalies during typhoon landings in Taiwan/Japan / Hai Peng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkInteger phase clock method with single-satellite ambiguity fixing and its application in LEO satellite orbit determination / Kai Shao in Acta Geodaetica et Cartographica Sinica, vol 50 n° 4 ([20/04/2021])PermalinkImpact of the third frequency GNSS pseudorange and carrier phase observations on rapid PPP convergences / Jiang Guo in GPS solutions, vol 25 n° 2 (April 2021)Permalink