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Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network / Jussi Leinonen in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
[article]
Titre : Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network Type de document : Article/Communication Auteurs : Jussi Leinonen, Auteur ; Daniele Nerini, Auteur ; Alexis Berne, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7211 - 7223 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] données météorologiques
[Termes IGN] épaisseur de nuage
[Termes IGN] image à basse résolution
[Termes IGN] image GOES
[Termes IGN] modèle atmosphérique
[Termes IGN] précipitation
[Termes IGN] processus stochastique
[Termes IGN] réduction d'échelle
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal convolutif
[Termes IGN] SuisseRésumé : (auteur) Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as “downscaling” in the atmospheric sciences: improving the spatial resolution of low-resolution images. The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications. Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resolution atmospheric fields for an input consisting of a low-resolution sequence of images of the same field. We test the GAN using two data sets: one consisting of radar-measured precipitation from Switzerland; the other of cloud optical thickness derived from the Geostationary Earth Observing Satellite 16 (GOES-16). We find that the GAN can generate realistic, temporally consistent super-resolution sequences for both data sets. The statistical properties of the generated ensemble are analyzed using rank statistics, a method adapted from ensemble weather forecasting; these analyses indicate that the GAN produces close to the correct amount of variability in its outputs. As the GAN generator is fully convolutional, it can be applied after training to input images larger than the images used to train it. It is also able to generate time series much longer than the training sequences, as demonstrated by applying the generator to a three-month data set of the precipitation radar data. The source code to our GAN is available at https://github.com/jleinonen/downscaling-rnn-gan. Numéro de notice : A2021-645 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3032790 Date de publication en ligne : 02/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3032790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98349
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7211 - 7223[article]Variational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
[article]
Titre : Variational bayesian compressive multipolarization indoor radar imaging Type de document : Article/Communication Auteurs : Van Ha Tang, Auteur ; Abdesselam Bouzerdoum, Auteur ; Son Lam Phung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7459 - 7474 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] acquisition comprimée
[Termes IGN] détection à travers-le-mur
[Termes IGN] estimation bayesienne
[Termes IGN] fouillis d'échos
[Termes IGN] image radar
[Termes IGN] inférence statistique
[Termes IGN] modèle stochastique
[Termes IGN] polarisation
[Termes IGN] positionnement en intérieur
[Termes IGN] reconstruction d'imageRésumé : (auteur) This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced. Numéro de notice : A2021-647 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3051955 Date de publication en ligne : 26/01/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3051955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98354
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7459 - 7474[article]Vectorial integer bootstrapping: flexible integer estimation with application to GNSS / Peter J.G. Teunissen in Journal of geodesy, vol 95 n° 9 (September 2021)
[article]
Titre : Vectorial integer bootstrapping: flexible integer estimation with application to GNSS Type de document : Article/Communication Auteurs : Peter J.G. Teunissen, Auteur ; L. Massarweh, Auteur ; Sandra Verhagen, Auteur Année de publication : 2021 Article en page(s) : n° 99 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] ambiguïté entière
[Termes IGN] Bootstrap (statistique)
[Termes IGN] estimateur
[Termes IGN] mesurage de phase
[Termes IGN] phase GNSS
[Termes IGN] positionnement par GNSS
[Termes IGN] résolution d'ambiguïté
[Termes IGN] transformation en ZRésumé : (auteur) In this contribution, we extend the principle of integer bootstrapping (IB) to a vectorial form (VIB). The mathematical definition of the class of VIB-estimators is introduced together with their pull-in regions and other properties such as probability bounds and success rate approximations. The vectorial formulation allows sequential block-by-block processing of the ambiguities based on a user-chosen partitioning. In this way, flexibility is created, where for specific choices of partitioning, tailored VIB-estimators can be designed. This wide range of possibilities is discussed, supported by numerical simulations and analytical examples. Further guidelines are provided, as well as the possible extension to other classes of estimators. Numéro de notice : A2021-620 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01552-2 Date de publication en ligne : 16/08/2021 En ligne : https://doi.org/10.1007/s00190-021-01552-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98236
in Journal of geodesy > vol 95 n° 9 (September 2021) . - n° 99[article]Background segmentation in multicolored illumination environments / Nikolas Ladas in The Visual Computer, vol 37 n° 8 (August 2021)
[article]
Titre : Background segmentation in multicolored illumination environments Type de document : Article/Communication Auteurs : Nikolas Ladas, Auteur ; Paris Kaimakis, Auteur ; Yiorgos Chrysanthou, Auteur Année de publication : 2021 Article en page(s) : pp 2221 - 2233 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification pixellaire
[Termes IGN] détection d'ombre
[Termes IGN] éclairage
[Termes IGN] éclairement lumineux
[Termes IGN] modèle stochastique
[Termes IGN] objectif grand angulaire
[Termes IGN] réflectance
[Termes IGN] segmentation d'imageRésumé : (auteur) We present an algorithm for the segmentation of images into background and foreground regions. The proposed algorithm utilizes a physically based formulation of scene appearance which explicitly models the formation of shadows originating from color light sources. This formulation enables a probabilistic model to distinguish between shadows and foreground objects in challenging images. A key component of the proposed method is an algorithm for estimating the illumination arriving at the scene. We evaluate our algorithm using synthetic and real-world data and show that the proposed method performs favorably against other commonly used segmentation methods. Numéro de notice : A2021-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01981-8 Date de publication en ligne : 06/10/2020 En ligne : https://doi.org/10.1007/s00371-020-01981-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98225
in The Visual Computer > vol 37 n° 8 (August 2021) . - pp 2221 - 2233[article]Calibration of the process-based model 3-PG for major central European tree species / David I. Forrester in European Journal of Forest Research, vol 140 n° 4 (August 2021)
[article]
Titre : Calibration of the process-based model 3-PG for major central European tree species Type de document : Article/Communication Auteurs : David I. Forrester, Auteur ; Martina Lena Hobi, Auteur ; Amanda S. Mathys, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 847 - 868 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière
[Termes IGN] changement climatique
[Termes IGN] estimation bayesienne
[Termes IGN] étalonnage de modèle
[Termes IGN] Europe centrale
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de croissance végétale
[Termes IGN] modélisation de la forêt
[Termes IGN] peuplement mélangé
[Termes IGN] régression
[Termes IGN] Suisse
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Process-based forest models are important tools for predicting forest growth and their vulnerability to factors such as climate change or responses to management. One of the most widely used stand-level process-based models is the 3-PG model (Physiological Processes Predicting Growth), which is used for applications including estimating wood production, carbon budgets, water balance and susceptibility to climate change. Few 3-PG parameter sets are available for central European species and even fewer are appropriate for mixed-species forests. Here we estimated 3-PG parameters for twelve major central European tree species using 1418 long-term permanent forest monitoring plots from managed forests, 297 from un-managed forest reserves and 784 Swiss National Forest Inventory plots. A literature review of tree physiological characteristics, as well as regression analyses and Bayesian inference, were used to calculate the 3-PG parameters. The Swiss-wide calibration, based on monospecific plots, showed a robust performance in predicting forest stocks such as stem, foliage and root biomass. The plots used to inform the Bayesian calibration resulted in posterior ranges of the calibrated parameters that were, on average, 69% of the prior range. The bias of stem, foliage and root biomass predictions was generally less than 20%, and less than 10% for several species. The parameter sets also provided reliable predictions of biomass and mean tree sizes in mixed-species forests. Given that the information sources used to develop the parameters included a wide range of climatic, edaphic and management conditions and long time spans (from 1930 to present), these species parameters for 3-PG are likely to be appropriate for most central European forests and conditions. Numéro de notice : A2021-717 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-021-01370-3 Date de publication en ligne : 18/03/2021 En ligne : https://doi.org/10.1007/s10342-021-01370-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98630
in European Journal of Forest Research > vol 140 n° 4 (August 2021) . - pp 847 - 868[article]Structure-aware indoor scene reconstruction via two levels of abstraction / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkDeformation analysis of a reference wall towards the uncertainty investigation of terrestrial laser scanners / Berit Schmitz in Journal of applied geodesy, vol 15 n° 3 (July 2021)PermalinkIonospheric irregularity layer height and thickness estimation with a GNSS receiver array / Seebany Datta-Barua in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkLayout graph model for semantic façade reconstruction using laser point clouds / Hongchao Fan in Geo-spatial Information Science, vol 24 n° 3 (July 2021)PermalinkVectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)PermalinkA framework to manage uncertainty in the computation of waste collection routes after a flood / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2021 (July 2021)PermalinkMapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkRobust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds / Reza Maalek in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkSpatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images / Bin Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkUncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)Permalink