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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)
[article]
Titre : Data assimilation of growing stock volume using a sequence of remote sensing data from different sensors Titre original : Assimilation de données de volume de bois à l’aide d’une séquence de données de télédétection provenant de différents capteurs Type de document : Article/Communication Auteurs : Niels Lindgren, Auteur ; Hakan Olsson, Auteur ; Kenneth Nyström, Auteur ; Mattias Nyström, Auteur ; Göran Stahl, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Betula (genre)
[Termes IGN] capital sur pied
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage des données
[Termes IGN] filtre de Kalman
[Termes IGN] forêt boréale
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] Quercus (genre)
[Termes IGN] Suède
[Termes IGN] volume en boisRésumé : (auteur) Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show in this study that the accuracy of ALS predictions of growing stock volume can be maintained and even improved over time if they are forecasted and assimilated with more frequent but less accurate remote sensing data sources like satellite images, digital photogrammetry, and InSAR. We obtained these results by introducing important methodological adaptations to data assimilation compared to previous forestry studies in Sweden. On a test site in the southwest of Sweden (58°27′N, 13°39′E), we evaluated the performance of the extended Kalman filter and a proposed modified filter that accounts for error correlations. We also applied classical calibration to the remote sensing predictions. We evaluated the developed methods using a dataset with nine different acquisitions of remotely sensed data from a mix of sensors over four years, starting and ending with ALS-based predictions of growing stock volume. The results showed that the modified filter and the calibrated predictions performed better than the standard extended Kalman filter and that at the endpoint the prediction based on data assimilation implied an improved accuracy (25.0% RMSE), compared to a new ALS-based prediction (27.5% RMSE). Numéro de notice : A2022-144 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/07038992.2021.1988542 Date de publication en ligne : 17/10/2021 En ligne : https://doi.org/10.1080/07038992.2021.1988542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99985
in Canadian journal of remote sensing > vol 48 n° 2 (April 2022) . - pp[article]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)
[article]
Titre : Detection and mitigation of GNSS spoofing via the pseudorange difference between epochs in a multicorrelator receiver Type de document : Article/Communication Auteurs : Xiangyong Shang, Auteur ; Fuping Sun, Auteur ; Lundong Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] anti-leurrage
[Termes IGN] atténuation du signal
[Termes IGN] brouillage
[Termes IGN] détection de leurrage
[Termes IGN] détection du signal
[Termes IGN] filtre de Kalman
[Termes IGN] mesurage de pseudo-distance
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] qualité du signal
[Termes IGN] récepteur GNSS
[Termes IGN] signal GNSSRésumé : (auteur) Spoofing attacks have become an increasing threat to global navigation satellite system receivers. Existing anti-spoofing algorithms concentrate on the detection of these attacks; however, they are unable to prevent the counterfeit signal, which causes false position and timing results. Some defense techniques require the assistance of other sensors or measurement devices located at different positions. These impose many restrictions on the practical applications of anti-spoofing algorithms. In this study, the multicorrelator estimator, designed initially to prevent multipath signals, is applied to detect and mitigate spoofing. A statistic is proposed for spoofing detection based on the code phase difference between counterfeit and authentic signals. This statistic can significantly reduce the rate of false and missed alarms. Assuming there is no spoofing at the beginning, the pseudorange difference between epochs is derived for spoofing validation, allowing spoofing suppression in a single receiver. Based on this study, an estimation-validation-mitigation structure is presented. A robust extended Kalman filter is proposed to reduce gross errors in the multicorrelator measurements and improve estimation accuracy. Public-spoofing datasets recorded in real environments were used to verify the performance of different parameters. A total of 81 complex correlators were introduced in the experiments. Results show that using the proposed scheme, the position or time offsets caused by spoofing drop from 600 m to approximately 20 m, and the spoofing is mitigated considerably. The proposed method provides an effective anti-spoofing structure that requires only a single antenna and does not require additional sensors. Numéro de notice : A2022-108 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01224-4 Date de publication en ligne : 16/01/2022 En ligne : https://doi.org/10.1007/s10291-022-01224-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99610
in GPS solutions > vol 26 n° 2 (April 2022) . - n° 37[article]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)
[article]
Titre : An approach to extracting digital elevation model for undulating and hilly terrain using de-noised stereo images of Cartosat-1 sensor Type de document : Article/Communication Auteurs : Litesh Bopche, Auteur ; Priti P. Rege, Auteur Année de publication : 2022 Article en page(s) : pp 39 - 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] filtrage du bruit
[Termes IGN] image ALOS
[Termes IGN] image Cartosat-1
[Termes IGN] Inde
[Termes IGN] MNS ASTER
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] modèle stéréoscopique
[Termes IGN] points homologuesRésumé : (auteur) A digital elevation model (DEM) is established as an essential geospatial dataset requisite for many topographical and environmental applications. The freely available DEMs have low spatial resolution (SR ≥ 30 m) and comprise considerable vertical errors. The vertical errors are worsened in the undulating and hilly or rugged terrain regions. In this research, we introduced a study to investigate the effect of the noise reduction filters on the accuracy and quality of the DEMs for undulating and hilly terrain regions. The main objectives are to extract a high-quality DEM without collecting physical data like ground control points. DEM generation using de-noised stereo images is carried out using Rational Polynomial Coefficients of Cartosat-1 sensor and Automated Tie Point (ATP) selection. The ATP selection and distribution on the stereo images play a significant role in the DEM accuracy. The present paper also provides information about the optimum number of ATPs used for different topographic conditions. The altitude value of extracted DEM through de-noised stereo images and freely accessible DEMs is compared with reference to the ground truth value of the study region. The 3-D surface profile map of the DEM is used for visual interpretation. Numéro de notice : A2022-216 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-021-00412-0 Date de publication en ligne : 26/11/2021 En ligne : https://doi.org/10.1007/s12518-021-00412-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100086
in Applied geomatics > vol 14 n° 1 (March 2022) . - pp 39 - 55[article]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)
[article]
Titre : A method of vision aided GNSS positioning using semantic information in complex urban environment Type de document : Article/Communication Auteurs : Rui Zhai, Auteur ; Yunbin Yuan, Auteur Année de publication : 2022 Article en page(s) : n° 869 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] apprentissage profond
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] centrale inertielle
[Termes IGN] filtre de Kalman
[Termes IGN] GNSS assisté pour la navigation
[Termes IGN] information sémantique
[Termes IGN] milieu urbain
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement par GNSS
[Termes IGN] segmentation sémantique
[Termes IGN] système de numérisation mobile
[Termes IGN] vision par ordinateurRésumé : (auteur) High-precision localization through multi-sensor fusion has become a popular research direction in unmanned driving. However, most previous studies have performed optimally only in open-sky conditions; therefore, high-precision localization in complex urban environments required an urgent solution. The complex urban environments employed in this study include dynamic environments, which result in limited visual localization performance, and highly occluded environments, which yield limited global navigation satellite system (GNSS) performance. In order to provide high-precision localization in these environments, we propose a vision-aided GNSS positioning method using semantic information by integrating stereo cameras and GNSS into a loosely coupled navigation system. To suppress the effect of dynamic objects on visual positioning accuracy, we propose a dynamic-simultaneous localization and mapping (Dynamic-SLAM) algorithm to extract semantic information from images using a deep learning framework. For the GPS-challenged environment, we propose a semantic-based dynamic adaptive Kalman filtering fusion (S-AKF) algorithm to develop vision aided GNSS and achieve stable and high-precision positioning. Experiments were carried out in GNSS-challenged environments using the open-source KITTI dataset to evaluate the performance of the proposed algorithm. The results indicate that the dynamic-SLAM algorithm improved the performance of the visual localization algorithm and effectively suppressed the error spread of the visual localization algorithm. Additionally, after vision was integrated, the loosely-coupled navigation system achieved continuous high-accuracy positioning in GNSS-challenged environments. Numéro de notice : A2022-167 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.3390/rs14040869 Date de publication en ligne : 11/02/2022 En ligne : https://doi.org/10.3390/rs14040869 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99792
in Remote sensing > vol 14 n° 4 (February-2 2022) . - n° 869[article]GCN-Denoiser: mesh denoising with graph convolutional networks / Yuefan Shen in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)
[article]
Titre : GCN-Denoiser: mesh denoising with graph convolutional networks Type de document : Article/Communication Auteurs : Yuefan Shen, Auteur ; Hongbo Fu, Auteur ; Zhongshuo Du, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] filtrage du bruit
[Termes IGN] maille triangulaire
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal de graphesRésumé : (auteur) In this article, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks (GCNs). Unlike previous learning-based mesh denoising methods that exploit handcrafted or voxel-based representations for feature learning, our method explores the structure of a triangular mesh itself and introduces a graph representation followed by graph convolution operations in the dual space of triangles. We show such a graph representation naturally captures the geometry features while being lightweight for both training and inference. To facilitate effective feature learning, our network exploits both static and dynamic edge convolutions, which allow us to learn information from both the explicit mesh structure and potential implicit relations among unconnected neighbors. To better approximate an unknown noise function, we introduce a cascaded optimization paradigm to progressively regress the noise-free facet normals with multiple GCNs. GCN-Denoiser achieves the new state-of-the-art results in multiple noise datasets, including CAD models often containing sharp features and raw scan models with real noise captured from different devices. We also create a new dataset called PrintData containing 20 real scans with their corresponding ground-truth meshes for the research community. Our code and data are available at https://github.com/Jhonve/GCN-Denoiser. Numéro de notice : A2022-302 Affiliation des auteurs : non IGN Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1145/3480168 Date de publication en ligne : 09/02/2022 En ligne : https://doi.org/10.1145/3480168 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100373
in ACM Transactions on Graphics, TOG > Vol 41 n° 1 (February 2022) . - n° 8[article]GNSS/INS Kalman filter integrity monitoring with uncertain time correlated error processes / Omar Garcia Crespillo (2022)PermalinkA multipath and thermal noise joint error characterization and exploitation for low-cost GNSS PVT estimators in urban environment / Eustachio Roberto Matera (2022)PermalinkRecursive Gauss-Helmert model with equality constraints applied to the efficient system calibration of a 3D laser scanner / Sören Vogel in Journal of applied geodesy, vol 16 n° 1 (January 2022)PermalinkRobust GNSS carrier phase-based position and attitude estimation theory and applications / Daniel Arias Medina (2022)PermalinkPermalinkMobile mapping et PCRS / Clément Benoît in Géomatique expert, n° 136 (novembre - décembre 2021)PermalinkMulti-objective CNN-based algorithm for SAR despeckling / Sergio Vitale in IEEE Transactions on geoscience and remote sensing, vol 59 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)PermalinkImproving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency / Jiaqi Tian in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)PermalinkThe integration of GPS/BDS real-time kinematic positioning and visual–inertial odometry based on smartphones / Zun Niu in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkA constrained extended Kalman filter based on LS-VCE formulated by condition equations with prediction of cross-covariances / Vahid Mahboub in Survey review, Vol 53 n° 380 (September 2021)PermalinkDeep learning-based image de-raining using discrete Fourier transformation / Prasen Kumar Sharma in The Visual Computer, vol 37 n° 8 (August 2021)PermalinkUnsupervised denoising for satellite imagery using wavelet directional cycleGAN / Shaoyang Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkDeep learning in denoising of micro-computed tomography images of rock samples / Mikhail Sidorenko in Computers & geosciences, vol 151 (June 2021)PermalinkAn improved computerized ionospheric tomography model fusing 3-D multisource ionospheric data enabled quantifying the evolution of magnetic storm / Jian Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkSAR speckle removal using hybrid frequency modulations / Shuaiqi Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkSNR-based water height retrieval in rivers: Application to high amplitude asymmetric tides in the Garonne river / Pierre Zeiger in Remote sensing, vol 13 n° 9 (May-1 2021)PermalinkA stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms / Dimitrios Bellos in Machine Vision and Applications, vol 32 n° 3 (May 2021)PermalinkHyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkDenoising Sentinel-1 extra-wide mode cross-polarization images over sea ice / Yan Sun in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)Permalink