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Methods for quantification of systematic distance deviations under incidence angle with scanning total stations / Miriam Zámečníková in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)
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[article]
Titre : Methods for quantification of systematic distance deviations under incidence angle with scanning total stations Type de document : Article/Communication Auteurs : Miriam Zámečníková, Auteur ; Hans Neuner, Auteur Année de publication : 2018 Article en page(s) : pp 268 - 284 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie
[Termes IGN] angle d'incidence
[Termes IGN] erreur systématique
[Termes IGN] Multistation
[Termes IGN] tachéomètre électronique
[Termes IGN] télémètre laser terrestreRésumé : (Auteur) If scanning total stations (TLS+TS) are used in scanning mode for high accurate engineering applications, the systematic influence of the incidence angle (IA) on the reflectorless distance measurement has to be eliminated. At present, methods for quantifying the systematic distance deviations under IA are missing because the measured points are not reproducible. In this paper, three such methods are presented. They are conditional on the used instruments and the required accuracy. These methods are validated with respect to specified framework conditions. The distance deviations are derived in all three methods as difference between the distance measured with TLS+TS in the scanning mode (DTLS) and the corresponding reference distance (Dref). The Dref is determined in three steps: measurement of a high accuracy network, measurement for determining the starting point of the Dref; object measurement to determine the endpoints of Dref. The corresponding DTLS and Dref are identified by means of the horizontal direction Hz (HzTLS and Hzref) and the vertical angle V (VTLS and Vref), both pairs of angles referring to the same origin marked by the axis of the common coordinate system. Depending on the used method, the Dref is determined with a standard uncertainty of 0.1–0.3 mm (at a distance of 30 m). The quantified influence of IA on the distance measurement of the Leica MS50 at a distance of 30 m to a granite plate varies in the interval of 0.8 mm. The strong variation due to the IA occurs from 0 to 20 gon, its effect is stable from 20 to 60 gon. Numéro de notice : A2018-391 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.07.008 Date de publication en ligne : 03/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.07.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90825
in ISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 268 - 284[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018103 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)
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Titre : Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier Type de document : Article/Communication Auteurs : Huanxue Zhang, Auteur ; Qiangzi Li, Auteur ; Jiangui Liu, Auteur ; Taifeng Dong, Auteur ; Heather McNairn, Auteur Année de publication : 2018 Article en page(s) : pp 1017 - 1035 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] bande spectrale
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] corrélation par régions de niveaux de gris
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image SPOT 5
[Termes IGN] indice de végétation
[Termes IGN] limite de terrain
[Termes IGN] Ontario (Canada)
[Termes IGN] réflectance spectrale
[Termes IGN] segmentation d'image
[Termes IGN] surface cultivée
[Termes IGN] surveillance agricole
[Termes IGN] texture d'image
[Termes IGN] variogrammeRésumé : (auteur) In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classification. Numéro de notice : A2019-049 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1333533 Date de publication en ligne : 23/06/2017 En ligne : https://doi.org/10.1080/10106049.2017.1333533 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92063
in Geocarto international > vol 33 n° 10 (October 2018) . - pp 1017 - 1035[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2018041 RAB Revue Centre de documentation En réserve L003 Disponible PPPH : a MATLAB-based software for multi-GNSS precise point positioning analysis / Berkay Bahadur in GPS solutions, vol 22 n° 4 (October 2018)
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Titre : PPPH : a MATLAB-based software for multi-GNSS precise point positioning analysis Type de document : Article/Communication Auteurs : Berkay Bahadur, Auteur ; Metin Nohutcu, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] affaiblissement géométrique de la précision
[Termes IGN] données GNSS
[Termes IGN] erreur en position
[Termes IGN] horloge du récepteur
[Termes IGN] Matlab
[Termes IGN] positionnement ponctuel précis
[Termes IGN] retard troposphérique zénithal
[Vedettes matières IGN] Traitement de données GNSSRésumé : (Auteur) The integration of different GNSS constellations offers considerable opportunities to improve Precise Point Positioning (PPP) performance. Being aware of the limited number of the alternatives that utilize the potential advantages of the multi-constellation and multi-frequency GNSS, we developed a MATLAB-based GNSS analysis software, named PPPH. PPPH is capable of processing GPS, GLONASS, Galileo and BeiDou data, and forming their different combinations depending on user’s preference. Thanks to its user-friendly graphical interface, PPPH allows users to determine a variety of processing options and parameters. In addition to an output file including the estimated parameters for every single epoch, PPPH also presents several analyzing and plotting tools for evaluating the results, such as positioning error, tropospheric zenith total delay, receiver clock estimation, satellite number, dilution of precisions. On the other hand, we conducted experimental tests to both validate the performance of PPPH and assess the potential benefits of multi-GNSS on PPP. The results indicate that PPPH provides comparable PPP solution with the general standards and also contributes to the improvement of PPP performance with the integration of multi-GNSS. Consequently, we introduce a GNSS analysis software that is easy to use, has a robust performance and is open to progress with its modular structure. Numéro de notice : A2018-377 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-018-0777-z Date de publication en ligne : 18/08/2018 En ligne : https://doi.org/10.1007/s10291-018-0777-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90781
in GPS solutions > vol 22 n° 4 (October 2018)[article]Assessment of local GNSS baselines at co-location sites / Iván Herrera Pinzón in Journal of geodesy, vol 92 n° 9 (September 2018)
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Titre : Assessment of local GNSS baselines at co-location sites Type de document : Article/Communication Auteurs : Iván Herrera Pinzón, Auteur ; Markus Rothacher, Auteur Année de publication : 2018 Article en page(s) : pp 1079 - 1095 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] co-positionnement
[Termes IGN] erreur systématique
[Termes IGN] International Terrestrial Reference Frame
[Termes IGN] ligne de base
[Termes IGN] point de liaison (géodésie)
[Termes IGN] positionnement par GNSS
[Termes IGN] retard troposphérique zénithal
[Termes IGN] série temporelle
[Termes IGN] station permanenteRésumé : (Auteur) As one of the major contributors to the realisation of the International Terrestrial Reference System (ITRS), the Global Navigation Satellite Systems (GNSS) are prone to suffer from irregularities and discontinuities in time series. While often associated with hardware/software changes and the influence of the local environment, these discrepancies constitute a major threat for ITRS realisations. Co-located GNSS at fundamental sites, with two or more available instruments, provide the opportunity to mitigate their influence while improving the accuracy of estimated positions by examining data breaks, local biases, deformations, time-dependent variations and the comparison of GNSS baselines with existing local tie measurements. With the use of co-located GNSS data from a subset sites of the International GNSS Service network, this paper discusses a global multi-year analysis with the aim of delivering homogeneous time series of coordinates to analyse system-specific error sources in the local baselines. Results based on the comparison of different GNSS-based solutions with the local survey ties show discrepancies of up to 10 mm despite GNSS coordinate repeatabilities at the sub-mm level. The discrepancies are especially large for the solutions using the ionosphere-free linear combination and estimating tropospheric zenith delays, thus corresponding to the processing strategy used for global solutions. Snow on the antennas causes further problems and seasonal variations of the station coordinates. These demonstrate the need for a permanent high-quality monitoring of the effects present in the short GNSS baselines at fundamental sites. Numéro de notice : A2018-459 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-017-1108-9 Date de publication en ligne : 17/01/2018 En ligne : https://doi.org/10.1007/s00190-017-1108-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91056
in Journal of geodesy > vol 92 n° 9 (September 2018) . - pp 1079 - 1095[article]Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)
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Titre : Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; R. Prasad, Auteur ; D. K. Gupta, Auteur ; V. N. Mishra, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 942 - 956 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance végétale
[Termes IGN] cultures
[Termes IGN] données polarimétriques
[Termes IGN] estimation statistique
[Termes IGN] hiver
[Termes IGN] image Sentinel-SAR
[Termes IGN] Leaf Area Index
[Termes IGN] régression
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste marge
[Termes IGN] teneur en eau de la végétationRésumé : (Auteur) In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m2 area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R2 = 0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R2 = 0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms. Numéro de notice : A2018-337 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1316781 Date de publication en ligne : 18/04/2017 En ligne : https://doi.org/10.1080/10106049.2017.1316781 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90551
in Geocarto international > vol 33 n° 9 (September 2018) . - pp 942 - 956[article]Research on the estimation model of vegetation water content in halophyte leaves based on the newly developed vegetation indices / Zhe Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 9 (September 2018)
PermalinkVisible + Near Infrared spectroscopy as taxonomic tool for identifying birch species / Mulualem Tigabu in Silva fennica, vol 52 n° 4 (September 2018)
PermalinkICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
PermalinkMulti-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data / Siddhartha Khare in Geocarto international, vol 33 n° 7 (July 2018)
PermalinkPrecise orbit determination of the Sentinel-3A altimetry satellite using ambiguity-fixed GPS carrier phase observations / Oliver Montenbruck in Journal of geodesy, vol 92 n° 7 (July 2018)
PermalinkThe impact of solid Earth-tide model error on tropospheric zenith delay estimates and GPS coordinate time series / Fei Li in Survey review, vol 50 n° 361 (July 2018)
PermalinkA two-stage tropospheric correction model combining data from GNSS and numerical weather model / Jan Douša in GPS solutions, vol 22 n° 3 (July 2018)
PermalinkUsing kites for 3-D mapping of gullies at decimetre-resolution over several square kilometres: a case study on the Kamech catchment, Tunisia / Denis Feurer in Natural Hazards and Earth System Sciences, vol 18 n° 6 ([01/06/2018])
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PermalinkConnecting infrared spectra with plant traits to identify species / Maria F. Buitrago in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)
PermalinkModeling tropospheric wet delays with national GNSS reference network in China for BeiDou precise point positioning / Fu Zheng in Journal of geodesy, vol 92 n° 5 (May 2018)
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