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Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)
[article]
Titre : Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning Type de document : Article/Communication Auteurs : Malarvizhi Arulraj, Auteur ; Ana P. Baros, Auteur Année de publication : 2021 Article en page(s) : n° 112355 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Appalaches
[Termes IGN] apprentissage automatique
[Termes IGN] bande S
[Termes IGN] classification automatique
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image GPM
[Termes IGN] orographie
[Termes IGN] précipitationRésumé : (auteur) Ground-clutter is a significant cause of missed-detection and underestimation of precipitation in complex terrain from space-based radars such as the Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR). This research proposes an Artificial Intelligence (AI) framework consisting of a precipitation detection model (PDM) and a precipitation regime classification model (PCM) to improve orographic precipitation retrievals from GPM-DPR using machine learning. The PDM is a Random Forest Classifier using GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and low-level precipitation mixing ratios from the High-Resolution Rapid Refresh (HRRR) analysis as inputs. The PCM is a Convolutional Neural Network that predicts the precipitation regime class, defined independently based on quantitative features of ground-based radar reflectivity profiles, using GPM DPR Ku-band (Ku-PR) reflectivity profiles and GMI Tbs. The AI framework is demonstrated for warm-season precipitation in the Southern Appalachian Mountains over. Numéro de notice : A2021-279 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112355 Date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112355 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97372
in Remote sensing of environment > vol 257 (May 2021) . - n° 112355[article]Evaluating P-Band TomoSAR for biomass retrieval in boreal forest / Erik Blomberg in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)
[article]
Titre : Evaluating P-Band TomoSAR for biomass retrieval in boreal forest Type de document : Article/Communication Auteurs : Erik Blomberg, Auteur ; Lars M.H. Ulander, Auteur ; Stefano Tebaldini, Auteur ; Laurent Ferro-Famil, Auteur Année de publication : 2021 Article en page(s) : pp 3793 - 3804 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande P
[Termes IGN] biomasse forestière
[Termes IGN] forêt boréale
[Termes IGN] Suède
[Termes IGN] tomographie radarRésumé : (Auteur) P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden. BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%–36% (27–33 t/ha) for airborne data and 38%–39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity. Numéro de notice : A2021-339 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3020775 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3020775 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97570
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3793 - 3804[article]Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method / Hongliang Lu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)
[article]
Titre : Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method Type de document : Article/Communication Auteurs : Hongliang Lu, Auteur ; Heng Zhang, Auteur ; Huaitao Fan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 99 - 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande P
[Termes IGN] Chine
[Termes IGN] compensation
[Termes IGN] erreur de mesure
[Termes IGN] erreur de phase
[Termes IGN] Guyane (département français)
[Termes IGN] hauteur des arbres
[Termes IGN] image radar moirée
[Termes IGN] ligne de base
[Termes IGN] polarisation
[Termes IGN] tomographie radar
[Termes IGN] triangulation de DelaunayRésumé : (auteur) Synthetic aperture radar (SAR) tomography (TomoSAR) has been well-established for three-dimensional (3-D) information extraction of forests using the multi-baseline SAR data stacks. The multi-baseline SAR data stacks can be acquired by spaceborne and airborne SAR systems, but for forest scenarios, the data stacks acquired by the airborne SAR system are mostly used. Such a data stack has the advantages of short revisiting time and weak temporal decorrelation. However, due to the baseline errors (caused by the residual platform motion and the measurement errors of the navigation instruments), phase errors (PEs) will occur. PEs are independent of one track to the other, resulting in spreading and defocusing in tomographic imaging. In this paper, we proposed a novel phase compensation method named NC-PGA, which combines the methods of network construction (NC) and phase gradient autofocus (PGA) to estimate and compensate the PEs. The NC method uses the Delaunay triangulation network and beamforming to obtain an accurate elevation estimate of the selected permanent scatterers, which can be used as the prior information for subsequent processing to overcome the shortcomings of the PGA method in PEs estimation. The PGA method uses the spatial invariance of PEs in a limited area to compensate for the PE of each track. The applicability of the NC-PGA method is demonstrated using simulated data and real data. The real data contains two data stacks. The one is acquired by a full-polarization P-band airborne SAR system (developed independently by our project research team) over the study area in Saihanba Forest Farm in Hebei, China. The other one is acquired by ONERA SETHI airborne system over Paracou, French Guiana, in the frame of the European Space Agency’s campaign TropiSAR. We select a test area in the study area and successfully retrieve the height of the forest, and use LiDAR data for results validation and evaluation. Numéro de notice : A2021-271 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.022 Date de publication en ligne : 14/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.022 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97329
in ISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 99 - 118[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021051 SL Revue Centre de documentation Revues en salle Disponible 081-2021052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt 081-2021053 DEP-RECP Revue Saint-Mandé Dépôt en unité Exclu du prêt Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A / Olivier Bock in Earth System Science Data, vol 13 n° 5 (May 2021)
[article]
Titre : Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A Type de document : Article/Communication Auteurs : Olivier Bock , Auteur ; Pierre Bosser , Auteur ; Cyrille Flamant, Auteur ; Erik Doerflinger, Auteur ; Friedhelm Jansen, Auteur ; Romain Fagès , Auteur ; Sandrine Bony, Auteur ; Sabrina Schnitt, Auteur Année de publication : 2021 Projets : VEGAN / Bock, Olivier, EUREC4A / Bock, Olivier Article en page(s) : pp 2407 - 2436 Note générale : bibliographie
This work was supported by the CNRS program LEFE/INSU through the project VEGAN. The EUREC4A project was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 694768).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] Caraïbes
[Termes IGN] données auxiliaires
[Termes IGN] données GNSS
[Termes IGN] données météorologiques
[Termes IGN] erreur systématique
[Termes IGN] humidité de l'air
[Termes IGN] retard troposphérique zénithal
[Termes IGN] teneur intégrée en vapeur d'eauRésumé : (auteur) Ground-based Global Navigation Satellite System (GNSS) measurements from nearly fifty stations distributed over the Caribbean Arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality Integrated Water Vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the Tradewinds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD) to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a colocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS derived IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organisation for five representative GNSS stations across the Caribbean Arc and found that the environment of Fish cloud patterns to be moister than that of Flowers cloud patterns which, in turn, is moister than the environment of Gravel cloud patterns. The differences in the IWV means between Fish and Gravel were assessed to be significant. Finally, the Gravel moisture environment was found to be similar to that of clear, cloud-free conditions. The moisture environment associated with the Sugar cloud pattern has not been assessed because it was hardly observed during the first two months of 2020. The reprocessed ZTD and IWV data set from 49 GNSS stations used in this study are available from the AERIS data center (https://doi.org/10.25326/79; Bock (2020b)). Numéro de notice : A2021-172 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-13-2407-2021 Date de publication en ligne : 24/02/2021 En ligne : http://dx.doi.org/10.5194/essd-13-2407-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97213
in Earth System Science Data > vol 13 n° 5 (May 2021) . - pp 2407 - 2436[article]Integration of laser scanner and photogrammetry for heritage BIM enhancement / Yahya Alshawabkeh in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)
[article]
Titre : Integration of laser scanner and photogrammetry for heritage BIM enhancement Type de document : Article/Communication Auteurs : Yahya Alshawabkeh, Auteur ; Ahmad Baik, Auteur ; Yehia Miky, Auteur Année de publication : 2021 Article en page(s) : n° 316 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme ICP
[Termes IGN] Arabie Saoudite
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données TLS (télémétrie)
[Termes IGN] image captée par drone
[Termes IGN] lasergrammétrie
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] monument historique
[Termes IGN] ombre
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de pointsRésumé : (auteur) Digital 3D capture and reliable reproduction of architectural features is the first and most difficult step towards defining a heritage BIM. Three-dimensional digital survey technologies, such as TLS and photogrammetry, enable experts to scan buildings with a new level of detail. Challenges in the tracing of parametric objects in a TLS point cloud include the reconstruction of occluded parts, measurement of uncertainties relevant to surface reflectivity, and edge detection and location. In addition to image-based techniques being considered cost effective, highly flexible, and efficient in producing a high-quality 3D textured model, they also provide a better interpretation of surface linear characteristics. This article addresses an architecture survey workflow using photogrammetry and TLS to optimize a point cloud that is sufficient for a reliable HBIM. Fusion-based workflows were proposed during the recording of two heritage sites—the Matbouli House Museum in Historic Jeddah, a UNESCO World Heritage Site; and Asfan Castle. In the Matbouli House Museum building, which is rich with complex architectural features, multi-sensor recording was implemented at different resolutions and levels of detail. The TLS data were used to reconstruct the basic shape of the main structural elements, while the imagery’s superior radiometric data and accessibility were effectively used to enhance the TLS point clouds for improving the geometry, data interpretation, and parametric tracing of irregular objects in the facade. Furthermore, in the workflow that is considered to be the ragged terrain of the Castle of Asfan, here, the TLS point cloud was supplemented with UAV data in the upper building zones where the shadow data originated. Both datasets were registered using an ICP algorithm to scale the photogrammetric data and define their actual position in the construction system. The hybrid scans were imported and processed in the BIM environment. The building components were segmented and classified into regular and irregular surfaces, in order to perform detailed building information modeling of the architectural elements. The proposed workflows demonstrated an appropriate performance in terms of reliable and complete BIM mapping in the complex structures. Numéro de notice : A2021-511 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10050316 Date de publication en ligne : 08/05/2021 En ligne : https://doi.org/10.3390/ijgi10050316 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97678
in ISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 316[article]Observable quality assessment of broadband very long baseline interferometry system / Ming H. Xu in Journal of geodesy, vol 95 n° 5 (May 2021)PermalinkRefining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data / Jia He in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 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)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])PermalinkAtmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters / Quinten Vanhellemont in Remote sensing of environment, Vol 256 (April 2020)PermalinkAutomatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network / Jian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkCloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques / Miao Tian in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkExtraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkGraph convolutional networks by architecture search for PolSAR image classification / Hongying Liu in Remote sensing, vol 13 n° 7 (April-1 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