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Titre : La modélisation des eaux Type de document : Article/Communication Auteurs : Michel Kasser , Auteur
Année de publication : 2021 Article en page(s) : pp 41 - 41 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] altimétrie par radar
[Termes IGN] image SWOT
[Termes IGN] océanographie dynamique
[Termes IGN] océanographie spatiale
[Termes IGN] précision centimétrique
[Termes IGN] précision de localisation
[Termes IGN] salinité
[Termes IGN] surface de la mer
[Termes IGN] vague
[Termes IGN] variation temporelleRésumé : (Auteur) Grâce à l’altimétrie radar, il est possible de mesurer la hauteur de la surface des mers, avec des applications fortes pour la connaissance de la Terre. Numéro de notice : A2021-893 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99250
in Géomètre > n° 2197 (décembre 2021) . - pp 41 - 41[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 063-2021111 RAB Revue Centre de documentation En réserve L003 Disponible Multi-objective CNN-based algorithm for SAR despeckling / Sergio Vitale in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)
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[article]
Titre : Multi-objective CNN-based algorithm for SAR despeckling Type de document : Article/Communication Auteurs : Sergio Vitale, Auteur ; Giampaolo Ferraioli, Auteur ; Vito Pascazio, Auteur Année de publication : 2021 Article en page(s) : pp 9336 - 9349 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] chatoiement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] filtre de déchatoiement
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] restauration d'imageRésumé : (auteur) Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications, such as change detection, image restoration, segmentation, detection, and classification. With reference to the synthetic aperture radar (SAR) domain, the application of DL techniques is not straightforward due to the nontrivial interpretation of SAR images, especially caused by the presence of speckle. Several DL solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions, not involving SAR image properties. In this article, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of these terms is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties, and strong scatterers identification. Their combination allows balancing these effects. Moreover, a specifically designed architecture is proposed to effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared with the state-of-art despeckling algorithms, both from a quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous, and extremely heterogeneous. Numéro de notice : A2021-810 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3034852 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3034852 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98874
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 11 (November 2021) . - pp 9336 - 9349[article]A 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)
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[article]
Titre : A parameterization of the cloud scattering polarization signal derived from GPM observations for microwave fast radative transfer models Type de document : Article/Communication Auteurs : Victoria Sol Galligani, Auteur ; Die Wang, Auteur ; Paola Belen Corales, Auteur ; Catherine Prigent, Auteur Année de publication : 2021 Article en page(s) : pp 8968 - 8977 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] image GPM
[Termes IGN] image radar
[Termes IGN] latitude
[Termes IGN] modèle atmosphérique
[Termes IGN] modèle de transfert radiatif
[Termes IGN] nuage
[Termes IGN] polarisation
[Termes IGN] prévision météorologique
[Termes IGN] radiomètre à hyperfréquence
[Termes IGN] reconstruction du signal
[Termes IGN] variation saisonnièreRésumé : (auteur) Microwave cloud polarized observations have shown the potential to improve precipitation retrievals since they are linked to the orientation and shape of ice habits. Stratiform clouds show larger brightness temperature (TB) polarization differences (PDs), defined as the vertically polarized TB (TBV) minus the horizontally polarized TB (TBH), with ~10 K PD values at 89 GHz due to the presence of horizontally aligned snowflakes, while convective regions show smaller PD signals, as graupel and/or hail in the updraft tend to become randomly oriented. The launch of the global precipitation measurement (GPM) microwave imager (GMI) has extended the availability of microwave polarized observations to higher frequencies (166 GHz) in the tropics and midlatitudes, previously only available up to 89 GHz. This study analyzes one year of GMI observations to explore further the previously reported stable relationship between the PD and the observed TBs at 89 and 166 GHz, respectively. The latitudinal and seasonal variability is analyzed to propose a cloud scattering polarization parameterization of the PD-TB relationship, capable of reconstructing the PD signal from simulated TBs. Given that operational radiative transfer (RT) models do not currently simulate the cloud polarized signals, this is an alternative and simple solution to exploit the large number of cloud polarized observations available. The atmospheric radiative transfer simulator (ARTS) is coupled with the weather research and forecasting (WRF) model, in order to apply the proposed parameterization to the RT simulated TBs and hence infer the corresponding PD values, which show to reproduce the observed GMI PDs well. Numéro de notice : A2021-886 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3049921 Date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3049921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98871
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 11 (November 2021) . - pp 8968 - 8977[article]Persistent scatterer interferometry for Pettimudi (India) landslide monitoring using Sentinel-1A images / Hari Shankar in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)
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[article]
Titre : Persistent scatterer interferometry for Pettimudi (India) landslide monitoring using Sentinel-1A images Type de document : Article/Communication Auteurs : Hari Shankar, Auteur ; Arijit Roy, Auteur ; Prakash Chauhan, Auteur Année de publication : 2021 Article en page(s) : pp 853 - 862 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] déformation de la croute terrestre
[Termes IGN] effondrement de terrain
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] Inde
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] précipitation
[Termes IGN] risque naturel
[Termes IGN] série temporelle
[Termes IGN] surveillance géologiqueRésumé : (Auteur) The continuous monitoring of land surface movement over time is of paramount importance for assessing landslide triggering factors and mitigating landslide hazards. This research focuses on measuring horizontal and vertical surface displacement due to a devastating landslide event in the west-facing slope of the Rajamala Hills, induced by intense rainfall. The landslide occurred in Pettimudi, a tea-plantation village of the Idukki district in Kerala, India, on August 6–7, 2020. The persistent-scatterer synthetic aperture radar interferometry (PSInSAR ) technique, along with the Stanford Method for Persistent Scatterers (StaMPS), was applied to investigate the land surface movement over time. A stack of 20 Sentinel-1A single-look complex images (19 interferograms) acquired in descending passes was used for PSInSAR processing. The line-of-sight (LOS ) displacement in long time series, and hence the average LOS velocity, was measured at each measurement-point location. The mean LOS velocity was decomposed into horizontal east–west (EW ) and vertical up–down velocity components. The results show that the mean LOS, EW, and up–down velocities in the study area, respectively, range from –18.76 to +11.88, –10.95 to +6.93, and –15.05 to +9.53 mm/y, and the LOS displacement ranges from –19.60 to +19.59 mm. The displacement values clearly indicate the instability of the terrain. The time-series LOS displacement trends derived from the applied PSInSAR technique are very useful for providing valuable inputs for disaster management and the development of disaster early-warning systems for the benefit of local residents. Numéro de notice : A2021-897 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00020R3 Date de publication en ligne : 01/11/2021 En ligne : https://doi.org/10.14358/PERS.21-00020R3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99275
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 11 (November 2021) . - pp 853 - 862[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021111 SL Revue Centre de documentation Revues en salle Disponible Automatic detection of inland water bodies along altimetry tracks for estimating surface water storage variations in the Congo basin / Frédéric Frappart in Remote sensing, vol 13 n° 19 (October-1 2021)
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[article]
Titre : Automatic detection of inland water bodies along altimetry tracks for estimating surface water storage variations in the Congo basin Type de document : Article/Communication Auteurs : Frédéric Frappart, Auteur ; Pierre Zeiger, Auteur ; Julie Betbeder, Auteur ; Valéry Gond, Auteur ; Régis Bellot , Auteur ; Nicolas Baghdadi, Auteur ; Fabien Blarel, Auteur ; José Darrozes, Auteur ; Luc Bourrel, Auteur ; Frédérique Seyler, Auteur
Année de publication : 2021 Projets : TOSCA / Article en page(s) : n° 3804 Note générale : bibliographie
This research was funded by CNES TOSCA grants number CASCHMIR and SWHYM.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification par nuées dynamiques
[Termes IGN] Congo (bassin)
[Termes IGN] détection automatique
[Termes IGN] données altimétriques
[Termes IGN] eau de surface
[Termes IGN] estimation statistique
[Termes IGN] image Envisat-ASAR
[Termes IGN] image Jason-AMR
[Termes IGN] niveau de l'eau
[Termes IGN] série temporelle
[Termes IGN] stockage
[Termes IGN] volume d'eau
[Termes IGN] zone humideRésumé : (auteur) Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimentation and erosion processes, and are a source for greenhouse gases. Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered. Numéro de notice : A2021-935 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13193804 Date de publication en ligne : 23/09/2021 En ligne : https://doi.org/10.3390/rs13193804 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99542
in Remote sensing > vol 13 n° 19 (October-1 2021) . - n° 3804[article]A feature based change detection approach using multi-scale orientation for multi-temporal SAR images / R. Vijaya Geetha in European journal of remote sensing, vol 54 sup 2 (2021)
PermalinkGPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings / Bin Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)
PermalinkInvestigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations / Caglar Bayik in Natural Hazards, vol 109 n° 1 (October 2021)
PermalinkOrbit error removal in InSAR/MTInSAR with a patch-based polynomial model / Yanan Du in International journal of applied Earth observation and geoinformation, vol 102 (October 2021)
PermalinkPhase unmixing of TerraSAR-X staring spotlight interferograms in building scale for PS height and deformation / Peng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)
PermalinkSeawater Debye model function at L-band and its impact on salinity retrieval from Aquarius satellite data / Yiwen Zhou in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)
PermalinkConiferous 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)
PermalinkSentinel-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)
PermalinkVariational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
PermalinkEstimation of surface deformation due to Pasni earthquake using RADAR interferometry / Muhammad Ali in Geocarto international, vol 36 n° 14 ([01/08/2021])
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