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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]

Thin cloud removal based on signal transmission principles and spectral mixture analysis / Meng Xu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1659 - 1669 Titre : Thin cloud removal based on signal transmission principles and spectral mixture analysis Type de document : Article/Communication Auteurs : Meng Xu, Auteur ; Mark Pickering, Auteur ; Antonio J. Plaza, Auteur ; Xiuping Jia, Auteur Année de publication : 2016 Article en page(s) : pp 1659 - 1669 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] classification pixellaire [Termes IGN] correction d'image [Termes IGN] épaisseur de nuage [Termes IGN] nuage [Termes IGN] rayonnement solaire [Termes IGN] reconstruction d'image Résumé : (Auteur) Cloud removal is an important goal for enhancing the utilization of optical remote sensing satellite images. Clouds dynamically affect the signal transmission due to their different shapes, heights, and distribution. In the case of thick opaque clouds, pixel replacement has been commonly adopted. For thin clouds, pixel correction techniques allow the effects of thin clouds to be removed while retaining the remaining information in the contaminated pixels. In this paper, we develop a new method based on signal transmission and spectral mixture analysis for pixel correction which makes use of a cloud removal model that considers not only the additive reflectance from the clouds but also the energy absorption when solar radiation passes through them. Data correction is achieved by subtracting the product of the cloud endmember signature and the cloud abundance and rescaling according to the cloud thickness. The proposed method has no requirement for meteorological data and does not rely on reference images. Our experimental results indicate that the proposed approach is able to perform effective removal of thin clouds in different scenarios. Numéro de notice : A2016-125 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2486780 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2486780 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80006 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2016031	SL	Revue	Centre de documentation	Revues en salle	Disponible

Removal of thin clouds using cirrus and QA bands of Landsat-8 / Yang Shen in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 9 (September 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 9 (September 2015) . - pp 721 - 731 Titre : Removal of thin clouds using cirrus and QA bands of Landsat-8 Type de document : Article/Communication Auteurs : Yang Shen, Auteur ; Yong Wang, Auteur ; Haitao Lv, Auteur ; Hong Li, Auteur Année de publication : 2015 Article en page(s) : pp 721 - 731 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande infrarouge [Termes IGN] coefficient de corrélation [Termes IGN] épaisseur de nuage [Termes IGN] image Landsat-8 [Termes IGN] nuage [Termes IGN] réflectance spectrale Résumé : (auteur) After atmospheric correction, an algorithm for the removal of thin cirrus cloud as well as alto-thin clouds or thin clouds collectively within visible and near infrared bands (Bands 1 through 5) of Landsat-8 was developed. The algorithm removed cirrus clouds using Band 9 first, and the remaining thin clouds using quality assurance (QA) band. Using a Landsat-8 sub-image of 129/39 (path/row) acquired on 16 December 2013, we evaluated the algorithm. Thin clouds disappeared visually. Reflectance values of Bands 1 through 4 decreased in both steps. Reflectance values of Band 5 decreased in step one, and then stayed the same. With a nearly cloud-free image acquired on 30 November 2013 as the “truth,” the spatial correlation coefficients of cloud-covered pixels within the December image were 0.84 or higher. Changes in reflectance values of Bands 1 to 5, and the high correlation coefficient values indicated the validity of the algorithm. Numéro de notice : A2015-985 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.9.721 En ligne : http://dx.doi.org/10.14358/PERS.81.9.721 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80266 [article]

Stereo cloud-top heights and cloud fraction retrieval from ATSR-2 / J.P. Muller in International Journal of Remote Sensing IJRS, vol 28 n° 9 (May 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n° 9 (May 2007) . - pp 1921 - 1938 Titre : Stereo cloud-top heights and cloud fraction retrieval from ATSR-2 Type de document : Article/Communication Auteurs : J.P. Muller, Auteur ; M.-A. Denis, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 1921 - 1938 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] appariement automatique [Termes IGN] données météorologiques [Termes IGN] épaisseur de nuage [Termes IGN] image ERS-ATSR [Termes IGN] température de luminance [Termes IGN] traitement automatique de données Résumé : (Auteur) An operational processing system is described for the automated retrieval of cloud-top heights (CTHs) and amounts from the Gridded Brightness Temperature (GBT) 1 km product derived from the dual view of the Along Track Scanning Radiometer (ATSR-2) instrument onboard the ESA ERS-2 satellite. A new stereo matching algorithm, called M4 is described together with the overall processing chain for retrieving cloud-top heights and amounts. M4 is based on the successful stereo matchers, M2 and M3 which have been in operation with the NASA MISR sensor since March 2000 and which have also been applied to mapping cloud fraction in the polar region using ATSR-2 (Cawkwell and Bamber 2002, Cawkwell et al. 2001). Results in companion papers (Naud et al., Denis et al. 2007) describe the accuracy achieved using this algorithm and detailed scene-by-scene results are available from http://cloudmap.org under results->UCL->ATSR-2 Validation. We assess the impact of cloud-top winds on the accuracy of the retrieved cloud-top heights and conclude that for most middle and lower tropospheric clouds, cloud-top winds will have little, if any, noticeable impact. We show how ATSR-2 cloud-top heights at the different wavelengths (particularly visible compared with thermal IR) can sometimes yield information on multi-layer clouds which is unique to ATSR-2. The processing system has recently been applied to three years of ASTR-2 GBT data and results are shown. These results are available from the British Atmospheric Data Centre. Copyright Taylor & Francis Numéro de notice : A2007-277 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160601030975 En ligne : https://doi.org/10.1080/01431160601030975 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28640 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07051	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt