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Calibration of a light hemispherical radiance field imaging system / Manchun Lei in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)
[article]
Titre : Calibration of a light hemispherical radiance field imaging system Type de document : Article/Communication Auteurs : Manchun Lei , Auteur ; Christian Thom , Auteur ; Christophe Meynard , Auteur ; Jean-Michaël Muller , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 195 - 202 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] étalonnage géométrique
[Termes IGN] étalonnage radiométrique
[Termes IGN] image hémisphérique
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] radiance
[Termes IGN] zone urbaineRésumé : (auteur) A light hemispherical radiance field imaging system based on fish-eye camera was developed for the measurement of the surface incident radiance in an urban environment, which is often affected by radiometric heterogeneity problems. A linear radiometric model and a polynomial fish-eye lens model are used. A temperature-dependent dark level model is proposed to improve the dark correction for high dynamic range photography. This paper describes the calibration procedure for spectral and geometrical radiance field measurements and presents the results of the calibration. The spectral radiometric calibration error is 2.07%, 1.34%, and 0.98% for blue, green, and red bands, respectively. The mean geometrical calibration error is 2.037 pixels. Numéro de notice : A2022-438 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-1-2022-195-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-1-2022-195-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100750
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-1-2022 (2022 edition) . - pp 195 - 202[article]Regional ionospheric corrections for high accuracy GNSS positioning / Tam Dao in Remote sensing, vol 14 n° 10 (May-2 2022)
[article]
Titre : Regional ionospheric corrections for high accuracy GNSS positioning Type de document : Article/Communication Auteurs : Tam Dao, Auteur ; Ken Harima, Auteur ; Brett Anthony Carter, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2463 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] Australie
[Termes IGN] Continuously Operating Reference Station network
[Termes IGN] correction ionosphérique
[Termes IGN] modèle ionosphérique
[Termes IGN] positionnement par GNSS
[Termes IGN] positionnement ponctuel précis
[Termes IGN] retard ionosphèriqueRésumé : (auteur) Centimetre-level accurate ionospheric corrections are required for a high accuracy and rapid convergence of Precise Point Positioning (PPP) GNSS positioning solutions. This research aims to evaluate the accuracy of a local/regional ionospheric delay model using a linear interpolation method across Australia. The accuracy of the ionospheric corrections is assessed as a function of both different latitudinal regions and the number and spatial density of GNSS Continuously Operating Reference Stations (CORSs). Our research shows that, for a local region of 5° latitude ×10° longitude in mid-latitude regions of Australia (~30° to 40°S) with approximately 15 CORS stations, ionospheric corrections with an accuracy of 5 cm can be obtained. In Victoria and New South Wales, where dense CORS networks exist (nominal spacing of ~100 km), the average ionospheric corrections accuracy can reach 2 cm. For sparse networks (nominal spacing of >200 km) at lower latitudes, the average accuracy of the ionospheric corrections is within the range of 8 to 15 cm; significant variations in the ionospheric errors of some specific satellite observations during certain periods were also found. In some regions such as Central Australia, where there are a limited number of CORSs, this model was impossible to use. On average, centimetre-level accurate ionospheric corrections can be achieved if there are sufficiently dense (i.e., nominal spacing of approximately 200 km) GNSS CORS networks in the region of interest. Based on the current availability of GNSS stations across Australia, we propose a set of 15 regions of different ionospheric delay accuracies with extents of 5° latitude ×10° longitude covering continental Australia. Numéro de notice : A2022-400 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs14102463 Date de publication en ligne : 20/05/2022 En ligne : https://doi.org/10.3390/rs14102463 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100703
in Remote sensing > vol 14 n° 10 (May-2 2022) . - n° 2463[article]Alternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation / Toshihiro Sakamoto in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)
[article]
Titre : Alternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation Type de document : Article/Communication Auteurs : Toshihiro Sakamoto, Auteur ; Daisuke Ogawa, Auteur ; Satoko Hiura, Auteur ; Nobusuke Iwasaki, Auteur Année de publication : 2022 Article en page(s) : pp 323 - 332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bande spectrale
[Termes IGN] blé (céréale)
[Termes IGN] chlorophylle
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] indice de végétation
[Termes IGN] orthophotoplan numérique
[Termes IGN] point d'appui
[Termes IGN] précision du positionnement
[Termes IGN] rizière
[Termes IGN] structure-from-motionRésumé : (Auteur) Vegetation indices (VIs), such as the green chlorophyll index and normalized difference vegetation index, are calculated from visible and near-infrared band images for plant diagnosis in crop breeding and field management. The DJI P4 Multispectral drone combined with the Agisoft Metashape Structure from Motion/Multi View Stereo software is some of the most cost-effective equipment for creating high-resolution orthomosaic VI images. However, the manufacturer's procedure results in remarkable location estimation inaccuracy (average error: 3.27–3.45 cm) and alignment errors between spectral bands (average error: 2.80–2.84 cm). We developed alternative processing procedures to overcome these issues, and we achieved a higher positioning accuracy (average error: 1.32–1.38 cm) and better alignment accuracy between spectral bands (average error: 0.26–0.32 cm). The proposed procedure enables precise VI analysis, especially when using the green chlorophyll index for corn, and may help accelerate the application of remote sensing techniques to agriculture. Numéro de notice : A2022-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00064R2 Date de publication en ligne : 01/05/2022 En ligne : https://doi.org/10.14358/PERS.21-00064R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101379
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 5 (May 2022) . - pp 323 - 332[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022052 SL Revue Centre de documentation Revues en salle Disponible 105-2022051 SL Revue Centre de documentation Revues en salle Disponible Efficient convolutional neural architecture search for LiDAR DSM classification / Aili Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 5 (May 2022)
[article]
Titre : Efficient convolutional neural architecture search for LiDAR DSM classification Type de document : Article/Communication Auteurs : Aili Wang, Auteur ; Dong Xue, Auteur ; Haibin Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5703317 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modèle de transfert radiatif
[Termes IGN] modèle numérique de surface
[Termes IGN] précision de la classification
[Termes IGN] semis de pointsRésumé : (auteur) Light detection and ranging (LiDAR) data provide rich elevation information, so it plays an irreplaceable role in ground object classification. Recently, convolutional neural networks (CNNs) have shown excellent performance in LiDAR digital surface models (DSMs) classification. However, the architecture of CNN model relies heavily on manual design, so it has great limitations. In addition, different sensors capture LiDAR datasets with different properties, so the model should be designed to suit for different datasets, which further increases the workload of architecture design. Therefore, this article proposes a method of automatic design of LiDAR DSM classification model. First, attention mechanism is introduced into search space to improve the feature extraction capability of the model. Then, a gradient-based search strategy is used to obtain the optimal architecture from this search space. Second, a learning rate adjustment strategy is proposed to reduce the time spent in the search stage and evaluation stage to improve the classification accuracy of the model. Finally, a regularization scheme is introduced to enhance the robustness of the model and avoid overfitting. Experimental results on three public LiDAR datasets (Bayview Park, Recology, and Houston) obtained from different sensors show that the proposed neural architecture search method achieves the impressive classification performance compared to several state-of-the-art classification methods and improves the classification accuracy under the condition of limited training samples. Numéro de notice : A2022-408 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3171520 Date de publication en ligne : 02/05/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3171520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100742
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 5 (May 2022) . - n° 5703317[article]Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China / Kezhen Yao in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)
[article]
Titre : Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China Type de document : Article/Communication Auteurs : Kezhen Yao, Auteur ; Saini Yang, Auteur ; Shengnan Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] dispersion
[Termes IGN] effondrement de terrain
[Termes IGN] Extreme Gradient Machine
[Termes IGN] modèle de simulation
[Termes IGN] régression linéaire
[Termes IGN] risque naturel
[Termes IGN] vulnérabilitéRésumé : (auteur) Landslide susceptibility assessment serves as a critical scientific reference for geohazard control, land use, and sustainable development planning. The existing research has not fully considered the potential impact of the spatial agglomeration and dispersion of landslides on assessments. This issue may cause a systematic evaluation bias when the field investigation data are insufficient, which is common due to limited human resources. Accordingly, this paper proposes two novel strategies, including a clustering algorithm and a preprocessing method, for these two ignored features to strengthen assessments, especially in high-susceptibility regions. Multiple machine learning models are compared in a case study of the city of Bijie (Guizhou Province, China). Then we generate the optimal susceptibility map and conduct two experiments to test the validity of the proposed methods. The primary conclusions of this study are as follows: (1) random forest (RF) was superior to other algorithms in the recognition of high-susceptibility areas and the portrayal of local spatial features; (2) the susceptibility map incorporating spatial feature messages showed a noticeable improvement over the spatial distribution and gradual change of susceptibility, as well as the accurate delineation of critical hazardous areas and the interpretation of historical hazards; and (3) the spatial distribution feature had a significant positive effect on modeling, as the accuracy increased by 5% and 10% after including the spatial agglomeration and dispersion consideration in the RF model, respectively. The benefit of the agglomeration is concentrated in high-susceptibility areas, and our work provides insight to improve the assessment accuracy in these areas, which is critical to risk assessment and prevention activities. Numéro de notice : A2022-371 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11050269 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.3390/ijgi11050269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100613
in ISPRS International journal of geo-information > vol 11 n° 5 (May 2022) . - n° 269[article]Multi-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)PermalinkA convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance / Shuo Shi in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkDirect photogrammetry with multispectral imagery for UAV-based snow depth estimation / Kathrin Maier in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkOn enhanced PPP with single difference between-satellite ionospheric constraints / Yan Xiang in Navigation : journal of the Institute of navigation, vol 69 n° 1 (Spring 2022)PermalinkPolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkAssessing ZWD models in delay and height domains using data from stations in different climate regions / Thainara Munhoz Alexandre de Lima in Applied geomatics, vol 14 n° 1 (March 2022)PermalinkDevelopment of a single-wavelength airborne bathymetric LiDAR: System design and data processing / Kai Guo in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)PermalinkExploiting light directionality for image-based 3D reconstruction of non-collaborative surfaces / Ali Karami in Photogrammetric record, vol 37 n° 177 (March 2022)PermalinkComprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event / Victoria Graffigna in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkCalibrating GNSS phase biases with onboard observations of low earth orbit satellites / Xingxing Li in Journal of geodesy, vol 96 n° 2 (February 2022)PermalinkDiffuse sunlight and cosmic rays: Missing pieces of the forest growth change attribution puzzle? / Jean-Daniel Bontemps in Science of the total environment, vol 806 n°1 (February 2022)PermalinkSurvival time and mortality rate of regeneration in the deep shade of a primeval beech forest / R. Petrovska in European Journal of Forest Research, vol 141 n° 1 (February 2022)PermalinkCo-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements / Mokhamad Nur Cahyadi in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkUse of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa / Mangana Rampheri in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkAttributs de texture extraits d'images multispectrales acquises en conditions d'éclairage non contrôlées : application à l'agriculture de précision / Anis Amziane (2022)PermalinkConventional and neural network-based water vapor density model for GNSS troposphere tomography / Chen Liu in GPS solutions, vol 26 n° 1 (January 2022)PermalinkDART: An efficient 3D Monte Carlo vector radiative transfer model for remote sensing applications / Yingjie Wang (2022)PermalinkPermalinkDevelopment of object detectors for satellite images by deep learning / Alissa Kouraeva (2022)PermalinkExamining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)PermalinkFusion de données hyperspectrales et panchromatiques dans le domaine réflectif / Yohann Constans (2022)PermalinkGenerating GPS decoupled clock products for precise point positioning with ambiguity resolution / Shuai Liu in Journal of geodesy, vol 96 n° 1 (January 2022)PermalinkGlobal canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)PermalinkIn situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region / Nadia Ouaadi (2022)PermalinkLarge-scale dimensional metrology for geodesy: First results from the European GeoMetre project / Florian Pollinger (2022)PermalinkModeling of precipitable water vapor from GPS observations using machine learning and tomography methods / Mir Reza Ghaffari Razin in Advances in space research, vol 69 n° 7 (April 2022)PermalinkNon-linear GNSS signal processing applied to land observation with high-rate airborne reflectometry / Hamza Issa (2022)PermalinkPython software to transform GPS SNR wave phases to volumetric water content / Angel Martín in GPS solutions, vol 26 n° 1 (January 2022)PermalinkLe radar révèle des montagnes cachées / Laurent Polidori in Géomètre, n° 2198 (janvier 2022)PermalinkPermalinkSalt tectonic imaging at crustal and experimental scales by seismic migration and adjoint method / Javier Abreu-Torres (2022)PermalinkSpatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde / Mir Reza Ghaffari Razin in GPS solutions, vol 26 n° 1 (January 2022)PermalinkBaseline-dependent clock offsets in VLBI data analysis / Hana Krásná in Journal of geodesy, vol 95 n° 12 (December 2021)PermalinkIonospheric corrections tailored to the Galileo High Accuracy Service / Adria Rovira-Garcia in Journal of geodesy, vol 95 n° 12 (December 2021)PermalinkMulti-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)PermalinkParticle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])PermalinkRadiative transfer modeling in structurally complex stands: towards a better understanding of parametrization / Frédéric André in Annals of Forest Science, vol 78 n° 4 (December 2021)PermalinkA CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms / Ibrahim Fayad in Remote sensing of environment, vol 265 (November 2021)PermalinkDiffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression / Forrest Corcoran in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkDownscaling MODIS spectral bands using deep learning / Rohit Mukherjee in GIScience and remote sensing, vol 58 n° 8 (2021)PermalinkFootprint size design of large-footprint full-waveform LiDAR for forest and topography applications: A theoretical study / Xuebo Yang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)PermalinkIonospheric tomographic common clock model of undifferenced uncombined GNSS measurements / German Olivares-Pulido in Journal of geodesy, vol 95 n° 11 (November 2021)PermalinkA mean-squared-error condition for weighting ionospheric delays in GNSS baselines / Peter J.G. Teunissen in Journal of geodesy, vol 95 n° 11 (November 2021)PermalinkA parameterization of the cloud scattering polarization signal derived from GPM observations for microwave fast radative transfer models / Victoria Sol Galligani 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)Permalink