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Postprocessing synchronization of a laser scanning system aboard a UAV / Marcela do Valle Machado in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 10 (October 2019) . - pp 753 - 763 Titre : Postprocessing synchronization of a laser scanning system aboard a UAV Type de document : Article/Communication Auteurs : Marcela do Valle Machado, Auteur ; Antonio Maria Garcia Tommaselli, Auteur ; Fernanda Magri Torres, Auteur ; Mariana Batista Campos, Auteur Année de publication : 2019 Article en page(s) : pp 753 - 763 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] décalage d'horloge [Termes descripteurs IGN] données altimétriques [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] positionnement inertiel [Termes descripteurs IGN] post-traitement [Termes descripteurs IGN] précision altimétrique [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] signal GNSS [Termes descripteurs IGN] synchronisation [Termes descripteurs IGN] télémétrie laser aéroporté [Termes descripteurs IGN] trajectoire Résumé : (Auteur) Synchronization of airborne laser scanning devices is a critical process that directly affects data accuracy. This process can be more challenging with low-cost airborne laser scanning (ALS) systems because some device connections from off-the-shelf sensors are less stable. An alternative to synchronization is performing a postprocessing clock correction. This article presents a technique for postprocessing synchronization (off-line) that estimates clock differences based on the correlation between the signals from the global navigation satellite system (GNSS) trajectory and the light detection and ranging (lidar) range, followed by refinement with a least-squares method. The correlation between signals was automatically estimated considering the planned flight maneuvers, in a flat terrain, to produce altimetric trajectory variations. Experiments were performed with an Ibeo LUX laser unit integrated with a NovAtel SPAN-IGM-S1 inertial navigation system that was transported by an unmanned aerial vehicle (UAV). The planimetric and altimetric accuracies of the point cloud obtained with the proposed postprocessing synchronization technique were 28 cm and 10 cm, respectively, at a flight height of 35 m. Numéro de notice : A2019-523 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.10.753 date de publication en ligne : 01/10/2019 En ligne : https://doi.org/10.14358/PERS.85.10.753 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93994 [article]

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On the application of Monte Carlo singular spectrum analysis to GPS position time series / Seyed Mohsen Khazraei in Journal of geodesy, vol 93 n° 9 (September 2019)  

Public [article]  inJournal of geodesy > vol 93 n° 9 (September 2019) . - pp 1401 - 1418 Titre : On the application of Monte Carlo singular spectrum analysis to GPS position time series Type de document : Article/Communication Auteurs : Seyed Mohsen Khazraei, Auteur ; AliReza Amiri-Simkooei, Auteur Année de publication : 2019 Article en page(s) : pp 1401 - 1418 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] algorithme de Cholesky [Termes descripteurs IGN] analyse de spectre singulier [Termes descripteurs IGN] bruit (théorie du signal) [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] bruit rose [Termes descripteurs IGN] coordonnées GPS [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] positionnement par GPS [Termes descripteurs IGN] série temporelle Résumé : (Auteur) Singular spectrum analysis (SSA) has recently been applied to various geodetic time series studies. As a data-adaptive method, SSA is capable of extracting signals with non-constant phase and amplitudes. Although SSA is a competent method in the presence of white noise, the contribution of colored noise, having semi-periodic behavior, degrades its performance. Parts of colored noise can be absorbed in the SSA eigenmodes, which specifies signals and hence resulting in spurious modulation or losing significant signals. Signals and colored noise are thus to be discriminated in the signal identification procedure. Monte Carlo SSA (MCSSA) in its original formulation, providing a significance test against the AR(1) noise null hypothesis, can be misinterpreted when other colored noise structures contribute to the series. We propose an algorithm for MCSSA that is not limited to the AR(1) noise hypothesis. It estimates the noise model parameters using LS-VCE and generates the surrogate data using the Cholesky decomposition. The algorithm is adapted to GPS position time series where the underlying noise is a combination of white noise and flicker noise. GPS position time series, postulated real situation, are first simulated to include annual and semiannual signals plus white and flicker noise. The results indicate that MCSSA can extract the annual and semiannual signals with 2.11 and 1.25 mm amplitudes (the global mean values) from 20-year-long time series, with 95% confidence level, if flicker noise is less than 17 and 13 mm/year1/4mm/year1/4, respectively. The longer the time series or the stronger the signals are, the higher these thresholds will be. This conclusion is also verified when applying MCSSA to the up component of GPS position time series of 347 JPL stations. Numéro de notice : A2019-505 Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-019-01253-x date de publication en ligne : 08/04/2019 En ligne : https://doi.org/10.1007/s00190-019-01253-x Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93788 [article]

Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach / Jian Zhao in Marine geodesy, vol 42 n° 4 (July 2019)  

Public [article]  inMarine geodesy > vol 42 n° 4 (July 2019) . - pp 344 - 366 Titre : Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach Type de document : Article/Communication Auteurs : Jian Zhao, Auteur ; Yanguo Fan, Auteur ; Yuxiang Mu, Auteur Année de publication : 2019 Article en page(s) : pp 344 - 366 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes descripteurs IGN] changement du niveau de la mer [Termes descripteurs IGN] détection d'anomalie [Termes descripteurs IGN] données altimétriques [Termes descripteurs IGN] données Jason [Termes descripteurs IGN] données Topex-Poseidon [Termes descripteurs IGN] image ERS-SAR [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] Pacifique nord [Termes descripteurs IGN] prévision [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] série temporelle Résumé : (auteur) Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2 cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast. Numéro de notice : A2019-281 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2019.1626306 date de publication en ligne : 12/06/2019 En ligne : https://doi.org/10.1080/01490419.2019.1626306 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93115 [article]

Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data / Piotr Tompalski in Remote sensing of environment, vol 227 (15 June 2019)  

Public [article]  inRemote sensing of environment > vol 227 (15 June 2019) . - pp 110 - 124 Titre : Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data Type de document : Article/Communication Auteurs : Piotr Tompalski, Auteur ; Joanne C. White, Auteur ; Nicholas C. Coops, Auteur ; Michael A. Wulder, Auteur Année de publication : 2019 Article en page(s) : pp 110 - 124 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] modèle mathématique [Termes descripteurs IGN] régression [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Airborne laser scanning (ALS) is a reliable source of accurate information for forest stand inventory attributes including height, cover, basal area, and volume. The commonly applied area-based approach (ABA) allows the derivation of wall-to-wall geospatial coverages representing each of the modeled attributes at a grid-cell level, with spatial resolutions typically between 20 and 30 m. The ABA predictive models are developed using stratified inventory data from field plots, the requirement for which can increase the overall cost of the ALS-based inventory. Parsimonious use of ground plots is a key means to control variable costs in the operational implementation of the ABA. In this paper, we demonstrate how the prediction accuracy of Lorey's height (HL, m), quadratic mean diameter (QMD, cm), and gross volume (V, m3) vary when existing ABA models are transferred to different areas or are applied to point cloud data with different characteristics than those on which the original model was developed. Specifically, we consider three scenarios of model transferability: (i) same point cloud characteristics, different areas; (ii) different point cloud characteristics, same areas; and (iii) different point cloud characteristics, different areas. We generated area-based models using three modeling approaches: linear regression (OLS), random forests (RF), and k-nearest neighbour (kNN) imputation. Results indicated that the prediction accuracy of area-based models varied by attribute and by modeling approach. We found that when the models were transferred their prediction accuracy decreased, with an average increase in relative bias up to 22.04%, and increase in relative RMSE up to 29.31%. Prediction accuracies for HL were higher than those of QMD or V when models were transferred, and had the lowest average increase in relative bias and relative RMSE of Numéro de notice : A2019-227 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.04.006 date de publication en ligne : 13/04/2019 En ligne : https://doi.org/10.1016/j.rse.2019.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92741 [article]

Helmert-VCE-aided fast-WTLS approach for global ionospheric VTEC modelling using data from GNSS, satellite altimetry and radio occultation / Andong Hu in Journal of geodesy, vol 93 n°6 (June 2019)  

Public [article]  inJournal of geodesy > vol 93 n°6 (June 2019) . - pp 877 - 888 Titre : Helmert-VCE-aided fast-WTLS approach for global ionospheric VTEC modelling using data from GNSS, satellite altimetry and radio occultation Type de document : Article/Communication Auteurs : Andong Hu, Auteur ; Zishen Li, Auteur ; Brett Carter, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 877 - 888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes descripteurs IGN] carte ionosphérique mondiale [Termes descripteurs IGN] données altimétriques [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] modèle ionosphérique [Termes descripteurs IGN] occultation du signal [Termes descripteurs IGN] pondération [Termes descripteurs IGN] retard ionosphèrique [Termes descripteurs IGN] teneur verticale totale en électrons [Termes descripteurs IGN] variance Résumé : (auteur) Vertical total electron content (VTEC) global ionospheric maps (GIM) are commonly used to correct the ionospheric delay of global navigation satellite system (GNSS) signals for single-frequency positioning and other ionospheric studies. The measurements observed by inhomogeneously distributed ground reference stations are the only data used to generate the GIMs. Thus the accuracy of the GIMs over ocean and polar regions is relatively poor due to the lack of measurements over these regions. In this study, space-borne VTECs obtained from ocean-altimetry and GNSS radio occultation measurements are incorporated into the modelling process. Since the three types of VTEC data have different qualities, the weight for each type of data is determined using the Helmert-variance component estimation (Helmert-VCE) method. In addition, unlike the traditional weighted least squares (WLS) estimation method in which the design matrix of observation equations is fixed, in this study, the design matrix, especially those elements in design matrix that are derived from the coordinates of either tangent point or ionospheric pierce point, are considered to be inaccurate. Thus they are adjusted together with the unknown coefficient parameters of the fitting model using the fast-weighted total least squares (fast-WTLS) technique. The proposed approach, named Helmert-WTLS, was tested using the data in the period of day of year (DOY) 217–224, 2016 and validated using GIMs produced by the research team for ionosphere and precise positioning based on BDS/GNSS (GIPP) at the Academy of Opto-Electronics, Chinese Academy of Sciences (CAS). Comparison results showed that the GIMs (with a 2 h temporal resolution) generated using the new approach can improve the determination of ionospheric TEC by 0.28 TEC units (TECU) over those from the Helmert-VCE-aided WLS approach (w.r.t CAS references, respectively) and by 1.61 TECU better than those from WLS, in terms of the mean of all root-mean-squares errors of all 2 h time slots in the 8-day testing period. In addition, in comparison with out-of-sample Jason-3 observations, results from the proposed method also outperformed Helmert-VCE-aided WLS, CAS and CODE models by 1.5, 2.4 and 2.4 TECU, respectively. Numéro de notice : A2019-352 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-018-1210-7 date de publication en ligne : 14/11/2018 En ligne : https://doi.org/10.1007/s00190-018-1210-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93398 [article]

BIM-Tracker: A model-based visual tracking approach for indoor localisation using a 3D building model / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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Integrated relative orientation based on point and line features via Plücker coordinates / Qinghong Sheng in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 4 (avril 2019)  

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Robust external calibration of terrestrial laser scanner and digital camera for structural monitoring / Mohammad Omidalizarandi in Journal of applied geodesy, vol 13 n° 2 (April 2019)  

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Vertical ionospheric delay estimation for single-receiver operation / Ahmed Elsayed in Journal of applied geodesy, vol 13 n° 2 (April 2019)  

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A comparative study between least square and total least square methods for time-series analysis and quality control of sea level observations / Mahmoud Pirooznia in Marine geodesy, vol 42 n° 2 (March 2019)  

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Geospatial data organization methods with emphasis on aperture-3 hexagonal discrete global grid systems / Ali Mahdavi Amiri in Cartographica, vol 54 n° 1 (spring 2019)  

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Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning / Qing Xu in Forest ecology and management, vol 434 (28 February 2019)  

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Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing / Minrui Zheng in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)  

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Least squares support vector machine model for coordinate transformation / Yao Yevenyo Ziggah in Geodesy and cartography, vol 45 n° 1 (2019)  

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Least-squares cross-wavelet analysis and its applications in geophysical time series / Ebrahim Ghaderpour in Journal of geodesy, vol 92 n° 10 (October 2018)  

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