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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7211 - 7223 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] Suisse Résumé : (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 [article]

Sentinel-1 sensitivity to soil moisture at high incidence angle and the impact on retrieval over seasonal crops / Davide Palmisano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7308 - 7321 Titre : Sentinel-1 sensitivity to soil moisture at high incidence angle and the impact on retrieval over seasonal crops Type de document : Article/Communication Auteurs : Davide Palmisano, Auteur ; Francesco Mattia, Auteur ; Anna Balenzano, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7308 - 7321 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse de sensibilité [Termes IGN] angle d'incidence [Termes IGN] bande C [Termes IGN] carte agricole [Termes IGN] Castille-et-Leon (Espagne) [Termes IGN] corrélation temporelle [Termes IGN] cultures [Termes IGN] humidité du sol [Termes IGN] image Sentinel-SAR [Termes IGN] Pouilles (Italie) [Termes IGN] réseau hydrographique [Termes IGN] rétrodiffusion [Termes IGN] transfert radiatif Résumé : (auteur) Approximately, 30% of the Sentinel-1 (S-1) swath over land is imaged with incidence angles higher than 40°. Still, the interplay among the scattering mechanisms taking place at such a high incidence and their implications on the backscatter information content is often disregarded. This article investigates, through an experimental and numerical study, the S-1 sensitivity to the surface soil moisture (SSM) over agricultural fields observed at low (~33°) and high (~43°) incidence angles and quantifies the impact of the incidence angle on the SSM retrieval accuracy. The study sites are the Apulian Tavoliere (Italy) and REd de MEDición de la HUmedad del Suelo (REMEDHUS) (Spain), which are both instrumented with a hydrologic network continuously measuring SSM. At low incidence angles, results confirm that for crops such as wheat and barley, dominated in C-band by surface scattering, there exists a good sensitivity of S-1 VV to SSM. At high incidence angles, the sensitivity to SSM holds through the combination of the soil attenuated and double bounce scattering. Conversely, over crops dominated by volume scattering, such as sugar beet, the S-1 VV signal is not correlated with the in situ SSM observations, neither at low nor at high incidence. For all the crops, the sensitivity of S-1 to SSM in VH is found significantly lower than in VV. The impact of the incidence angle on the SSM retrieval has been studied with a recursive algorithm based on a short-term change detection approach. An upper and lower bounds for the worsening of the S-1 VV retrieval performance at far versus near range observations have been estimated. In the worst-case scenario, the root mean square error (RMSE) increases from ~0.056 m 3 /m 3 , at low incidence, to ~0.071 m 3 /m 3 , at high incidence. The mechanism that lowers the retrieval accuracy at high incidence angles is further investigated in the synthetic experiment and its impact on the RMSE is estimated in terms of the volume scattering contribution. Numéro de notice : A2021-646 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3033887 Date de publication en ligne : 10/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3033887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98351 [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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7459 - 7474 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'image Résumé : (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 [article]

Coniferous and broad-leaved forest distinguishing using L-band polarimetric SAR data / Fang Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7487 - 7499 Titre : Coniferous and broad-leaved forest distinguishing using L-band polarimetric SAR data Type de document : Article/Communication Auteurs : Fang Shang, Auteur ; Taiga Saito, Auteur ; Saya Ohi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7487 - 7499 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande L [Termes IGN] détection de changement [Termes IGN] détection de cible [Termes IGN] distribution spatiale [Termes IGN] forêt de feuillus [Termes IGN] image ALOS-PALSAR [Termes IGN] image radar moirée [Termes IGN] Japon [Termes IGN] Pinophyta [Termes IGN] polarimétrie radar Résumé : (auteur) This article proposes a coniferous and broad-leaved forest distinguishing method using L-band polarimetric SAR data based on the structure-orientation parameter. The structure-orientation parameter is one of the averaged Stokes vector-based discriminators which is sensitive to the composition of equivalent horizontal and vertical structures. In the proposed method, the structure-orientation parameters is compensated by employing the scattered power information to remove the influence of the topography. The final distinguishing result is generated based on the statistical feature of the compensated parameters. The experiments using several sets of ALOS2-PALSAR2 level 1.1 data prove that the proposed method has high performance for forest-type distinguishing. Numéro de notice : A2021-648 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3032468 Date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3032468 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98355 [article]

Hyperspectral image fusion and multitemporal image fusion by joint sparsity / Han Pan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7887 - 7900 Titre : Hyperspectral image fusion and multitemporal image fusion by joint sparsity Type de document : Article/Communication Auteurs : Han Pan, Auteur ; Zhongliang Jing, Auteur ; Henry Leung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7887 - 7900 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] correction d'image [Termes IGN] flou [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] image multitemporelle [Termes IGN] image panchromatique [Termes IGN] pansharpening (fusion d'images) [Termes IGN] représentation parcimonieuse Résumé : (auteur) Different image fusion systems have been developed to deal with the massive amounts of image data for different applications, such as remote sensing, computer vision, and environment monitoring. However, the generalizability and versatility of these fusion systems remain unknown. This article proposes an efficient regularization framework to achieve different kinds of fusion tasks accounting for the spatiospectral and spatiotemporal variabilities of the fusion process. A joint minimization functional is developed by taking an advantage of a composite regularizer for enforcing joint sparsity in the gradient domain and the frame domain. The proposed composite regularizer is composed of the Hessian Schatten-norm regularization and contourlet-based regularization terms. The resulting problems are solved by the alternating direction method of multipliers (ADMM). The effectiveness of the proposed method is validated in a variety of image fusion experiments: 1) hyperspectral (HS) and panchromatic image fusion; 2) HS and multispectral image fusion; 3) multitemporal image fusion (MIF); and 4) multi-image deblurring. Results show promising performance compared with state-of-the-art fusion methods. Numéro de notice : A2021-649 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3039046 Date de publication en ligne : 07/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3039046 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98360 [article]