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Precise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
[article]
Titre : Precise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering Type de document : Article/Communication Auteurs : Ali Ozgun Ok, Auteur ; Asli Ozdarici-Ok, Auteur Année de publication : 2020 Article en page(s) : pp 557-569 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse de groupement
[Termes IGN] Citrus limon
[Termes IGN] détection de contours
[Termes IGN] état de l'art
[Termes IGN] extraction d'arbres
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle stochastique
[Termes IGN] TurquieRésumé : (Auteur) In this study, we present an original unified strategy for the precise extraction of individual citrus fruit trees from single digital surface model (DSM) input data. A probabilistic method combining the circular shape information with the knowledge of the local maxima in the DSM has been used for the detection of the candidate trees. An active contour is applied within each detected region to extract the borders of the objects. Thereafter, all extracted objects are seamlessly divided into clusters considering a new feature data set formed by (1) the properties of trees, (2) planting parameters, and (3) neighborhood relations. This original clustering stage has led to two new contributions: (1) particular objects or clustered structures having distinctive characters and relationships other than the citrus objects can be identified and eliminated, and (2) the information revealed by clustering can be used to recover missing citrus objects within and/or nearby each cluster. The main finding of this research is that a successful clustering can provide valuable input for identifying incorrect and missing information in terms of citrus tree extraction. The proposed strategy is validated in eight test sites selected from the northern part of Mersin province of Turkey. The results achieved are also compared with the state-of-the-art methods developed for tree extraction, and the success of the proposed unified strategy is clearly highlighted. Numéro de notice : A2020-491 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.9.557 Date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.14358/PERS.86.9.557 Format de la ressource électronique : LUR article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95933
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 9 (September 2020) . - pp 557-569[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2020091 SL Revue Centre de documentation Revues en salle Disponible Integration of airborne gravimetry data filtering into residual least-squares collocation: example from the 1 cm geoid experiment / Martin Willberg in Journal of geodesy, vol 94 n° 8 (August 2020)
[article]
Titre : Integration of airborne gravimetry data filtering into residual least-squares collocation: example from the 1 cm geoid experiment Type de document : Article/Communication Auteurs : Martin Willberg, Auteur ; Philipp Zingerle, Auteur ; Roland Pail, Auteur Année de publication : 2020 Article en page(s) : n° 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] collocation par moindres carrés
[Termes IGN] Colorado (Etats-Unis)
[Termes IGN] filtre passe-bas
[Termes IGN] géoïde gravimétrique
[Termes IGN] géoïde local
[Termes IGN] gravimétrie aérienne
[Termes IGN] levé gravimétrique
[Termes IGN] modèle stochastique
[Termes IGN] pondération
[Termes IGN] processus gaussienRésumé : (auteur) Low-pass filters are commonly used for the processing of airborne gravity observations. In this paper, for the first time, we include the resulting correlations consistently in the functional and stochastic model of residual least-squares collocation. We demonstrate the necessity of removing high-frequency noise from airborne gravity observations, and derive corresponding parameters for a Gaussian low-pass filter. Thereby, we intend an optimal combination of terrestrial and airborne gravity observations in the mountainous area of Colorado. We validate the combination in the frame of our participation in ‘the 1 cm geoid experiment’. This regional geoid modeling inter-comparison exercise allows the calculation of a reference solution, which is defined as the mean value of 13 independent height anomaly results in this area. Our result performs among the best and with 7.5 mm shows the lowest standard deviation to the reference. From internal validation we furthermore conclude that the input from airborne and terrestrial gravity observations is consistent in large parts of the target area, but not necessarily in the highly mountainous areas. Therefore, the relative weighting between these two data sets turns out to be a main driver for the final result, and is an important factor in explaining the remaining differences between various height anomaly results in this experiment. Numéro de notice : A2020-536 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01396-2 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.1007/s00190-020-01396-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95729
in Journal of geodesy > vol 94 n° 8 (August 2020) . - n° 75[article]Effect of spatial correlation on the performances of modernized GPS and Galileo in relative positioning / Noureddine Kheloufi in Geodesy and cartography, vol 46 n° 2 (July 2020)
[article]
Titre : Effect of spatial correlation on the performances of modernized GPS and Galileo in relative positioning Type de document : Article/Communication Auteurs : Noureddine Kheloufi, Auteur ; Abdelhalim Niati, Auteur Année de publication : 2020 Article en page(s) : pp 89 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] compensation Lambda
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] double différence
[Termes IGN] fréquence multiple
[Termes IGN] ligne de base
[Termes IGN] modèle stochastique
[Termes IGN] positionnement différentiel
[Termes IGN] positionnement par BeiDou
[Termes IGN] positionnement par Galileo
[Termes IGN] positionnement par GPS
[Termes IGN] résolution d'ambiguïté
[Termes IGN] retard ionosphèriqueRésumé : (auteur) In the context of processing GNSS (Global Navigation Satellite System) data, it is known that the estimation of the ionospheric delays decreases the strength of the observation model and makes significant the time required to fix the ambiguities namely in case of long baselines. However, considering the double-differenced (DD) ionospheric delays as stochastic quantities, the redundancy in this case increases and leads to the reduction of time of fixing the ambiguities. The approach developed in the present paper makes two considerations: 1) the DD ionospheric delays are assumed as stochastic quantities and, 2) the spatial correlation of errors is accounted for based on a simple model of correlation. A simulation is made and aims to study the effect of these two mentioned considerations on the performances of the three multifrequency GNSSs; modernized GPS, Galileo and BDS which are not yet in full capability. For each GNSS, dual-frequency combinations of frequencies as well as triple-frequency combination are investigated in the simulation. The performances studied include: the time to fix the ambiguities with high success rate and the precision of coordinates in static relative positioning with varying baseline length. A method is developed to derive what we call the spatial correlation model which approximately gives the covariance between the individual errors belonging to two stations. Furthermore, the stochastic models that follow from accounting and neglecting the spatial correlation are developed. The LAMBDA (Least-squares Ambiguity Decorrelation Adjustment) method is implemented for ambiguity decorrelation. The results show that the time to fix the ambiguities caused by accounting the spatial correlation is less than the time of fix without the spatial correlation. Also, a slight superiority of Galileo in terms of performances is seen compared to the other GNSS. For all the dualfrequency combinations investigated, when processing a baseline length of 500 km with accounted spatial correlation, the time needed to successfully fix the ambiguities lies between 5 and 9 min, whereas it becomes only between 2.5 and 3 min for all the triple-frequency combinations, this is with a sampling time of 5 s. In addition, for all different combinations, the coordinates precision is less than 8 mm even for 500 km. We think that these high performances result from: 1) the precise codes of future GNSS signals, 2) the high redundancy in the observations equation and, 3) taking into account the spatial correlation in the definition of the stochastic model. Numéro de notice : A2020-781 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3846/gac.2020.11009 Date de publication en ligne : 15/07/2020 En ligne : https://doi.org/10.3846/gac.2020.11009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96475
in Geodesy and cartography > vol 46 n° 2 (July 2020) . - pp 89 - 97[article]Stochastic modeling for VRS network-based GNSS RTK with residual interpolation uncertainty / Thanate Jongrujinan in Journal of applied geodesy, vol 14 n° 3 (July 2020)
[article]
Titre : Stochastic modeling for VRS network-based GNSS RTK with residual interpolation uncertainty Type de document : Article/Communication Auteurs : Thanate Jongrujinan, Auteur ; Chalermchon Satirapod, Auteur Année de publication : 2020 Article en page(s) : pp 317 – 325 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] correction atmosphérique
[Termes IGN] incertitude de position
[Termes IGN] interpolation
[Termes IGN] matrice de covariance
[Termes IGN] modèle stochastique
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement par GNSS
[Termes IGN] précision du positionnement
[Termes IGN] résolution d'ambiguïté
[Termes IGN] station virtuelle de référence
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) The key concept of the virtual reference station (VRS) network-based technique is to use the observables of multiple reference stations to generate the network corrections in the form of a virtual reference station at a nearby user’s location. Regarding the expected positioning accuracy, the novice GNSS data processing strategies have been adopted in the server-side functional model for mitigating distance-dependent errors including atmospheric effects and orbital uncertainty in order to generate high-quality virtual reference stations. In addition, the realistic stochastic model also plays an important role to take account of the unmodelled error in the rover-side processing. The results of our previous study revealed that the minimum norm quadratic unbiased estimation (MINQUE) stochastic model procedure can improve baseline component accuracy and integer ambiguity reliability, however, it requires adequate epoch length in a solution to calculate the elements of the variance-covariance matrix. As a result, it may not be suitable for urban environment where the satellite signal interruptions take place frequently, therefore, the ambiguity resolution needs to be resolved within the limited epochs. In order to address this limitation, this study proposed the stochastic model based on using the residual interpolation uncertainty (RIU) as the weighting schemes. This indicator reflects the quality of network corrections for any satellite pair at a specific rover position and can be calculated on the epoch-by-epoch basis. The comparison results with the standard stochastic model indicated that the RIU-weight model produced slightly better positioning accuracy but increased significant level of the ambiguity resolution successful rate. Numéro de notice : A2020-398 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0007 Date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.1515/jag-2020-0007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95433
in Journal of applied geodesy > vol 14 n° 3 (July 2020) . - pp 317 – 325[article]A probabilistic framework for improving reverse geocoding output / Zhengcong Yin in Transactions in GIS, Vol 24 n° 3 (June 2020)
[article]
Titre : A probabilistic framework for improving reverse geocoding output Type de document : Article/Communication Auteurs : Zhengcong Yin, Auteur ; Daniel W. Goldberg, Auteur ; Tracy A. Hammond, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 656 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] coordonnées GPS
[Termes IGN] géocodage inverse
[Termes IGN] géolocalisation
[Termes IGN] intégrité topologique
[Termes IGN] modèle stochastiqueRésumé : (auteur) Reverse geocoding, which transforms machine‐readable GPS coordinates into human‐readable location information, is widely used in a variety of location‐based services and analysis. The output quality of reverse geocoding is critical because it can greatly impact these services provided to end‐users. We argue that the output of reverse geocoding should be spatially close to and topologically correct with respect to the input coordinates, contain multiple suggestions ranked by a uniform standard, and incorporate GPS uncertainties. However, existing reverse geocoding systems often fail to fulfill these aims. To further improve the reverse geocoding process, we propose a probabilistic framework that includes: (1) a new workflow that can adapt all existing address models and unitizes distance and topology relations among retrieved reference data for candidate selections; (2) an advanced scoring mechanism that quantifies characteristics of the entire workflow and orders candidates according to their likelihood of being the best candidate; and (3) a novel algorithm that derives statistical surfaces for input GPS uncertainties and propagates such uncertainties into final output lists. The efficiency of the proposed approaches is demonstrated through comparisons to the four commercial reverse geocoding systems and through human judgments. We envision that more advanced reverse geocoding output ranking algorithms specific to different application scenarios can be built upon this work. Numéro de notice : A2020-444 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12623 Date de publication en ligne : 08/05/2020 En ligne : https://doi.org/10.1111/tgis.12623 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95507
in Transactions in GIS > Vol 24 n° 3 (June 2020) . - pp 656 - 680[article]Region level SAR image classification using deep features and spatial constraints / Anjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)PermalinkSelf-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors / Boris Kargoll in Journal of geodesy, vol 94 n° 5 (May 2020)PermalinkA single-receiver geometry-free approach to stochastic modeling of multi-frequency GNSS observables / Baocheng Zhang in Journal of geodesy, vol 94 n°4 (April 2020)PermalinkA spatio-temporal deformation model for laser scanning point clouds / Corinna Harmening in Journal of geodesy, vol 94 n°2 (February 2020)PermalinkProbabilistic pose estimation and 3D reconstruction of vehicles from stereo images / Maximilian Alexander Coenen (2020)PermalinkModelling of the timeseries of GNSS coordinates and their interaction with average magnitude earthquakes / Sanja Tucikesic in Geodetski vestnik, Vol 63 n° 4 (December 2019)PermalinkAddressing overfitting on point cloud classification using Atrous XCRF / Hasan Asy’ari Arief in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkEmpirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners / Tomislav Medic in Journal of applied geodesy, vol 13 n° 3 (July 2019)PermalinkInfluence of stochastic modeling for inter-system biases on multi-GNSS undifferenced and uncombined precise point positioning / Feng Zhou in GPS solutions, vol 23 n° 3 (July 2019)PermalinkStructural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)PermalinkSemantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)PermalinkRefining ionospheric delay modeling for undifferenced and uncombined GNSS data processing / Qile Zhao in Journal of geodesy, vol 93 n° 4 (April 2019)PermalinkThe stochastic model for Global Navigation Satellite Systems and terrestrial laser scanning observations: A proposal to account for correlations in least squares adjustment / Gaël Kermarrec in Journal of applied geodesy, vol 13 n° 2 (April 2019)PermalinkConditional random field and deep feature learning for hyperspectral image classification / Fahim Irfan Alam in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkLand cover classification in combined elevation and optical images supported by OSM data, mixed-level features, and non-local optimization algorithms / Dimitri Bulatov in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)PermalinkCorrecting rural building annotations in OpenStreetMap using convolutional neural networks / John E. 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Zhao in Survey review, vol 49 n° 354 (September 2017)PermalinkImpact of spatial correlations on the surface estimation based on terrestrial laser scanning / Tobias Jurek in Journal of applied geodesy, vol 11 n° 3 (September 2017)PermalinkA higher order conditional random field model for simultaneous classification of land cover and land use / Lena Albert in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkRobust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkPerformance evaluation of land change simulation models using landscape metrics / Sadeq Dezhkam in Geocarto international, vol 32 n° 6 (June 2017)PermalinkAnalyse de séries temporelles d’images Sentinel et intégration de connaissances pour la classification en milieu agricole / Simon Bailly (2017)PermalinkComparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)PermalinkComputationally efficient hyperspectral data learning based on the doubly stochastic dirichlet process / Xing Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkHyperspectral image classification with canonical correlation forests / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkPermalinkModèles géographiques avec le langage Mathematica / André Dauphiné (2017)PermalinkPré-segmentation pour la classification faiblement supervisée de scènes urbaines à partir de nuages de points 3D LIDAR / Stéphane Guinard (2017)PermalinkRandom-walker-based collaborative learning for hyperspectral image classification / Bin Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkWeakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds / Stéphane Guinard (2017)PermalinkDetermination of a terrestrial reference frame via Kalman filtering of very long baseline interferometry data / Benedikt Soja in Journal of geodesy, vol 90 n° 12 (December 2016)Permalink