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Termes IGN > imagerie > image radar > image radar moirée
image radar moiréeSynonyme(s)Interferogramme ;image SAR ;Image rso ;Image radar interférométrique Image par radar à antenne synthétiqueVoir aussi
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A deep learning framework for matching of SAR and optical imagery / Lloyd Haydn Hughes in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)
[article]
Titre : A deep learning framework for matching of SAR and optical imagery Type de document : Article/Communication Auteurs : Lloyd Haydn Hughes, Auteur ; Diego Marcos, Auteur ; Sylvain Lobry, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 166 - 179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] appariement d'images
[Termes IGN] apprentissage profond
[Termes IGN] données clairsemées
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] géoréférencement
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] superposition d'imagesRésumé : (auteur) SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. While for both data sources images are usually provided in a georeferenced manner, the geo-localization of optical images is often inaccurate due to propagation of angular measurement errors. Many methods for the matching of homologous image regions exist for both SAR and optical imagery, however, these methods are unsuitable for SAR-optical image matching due to significant geometric and radiometric differences between the two modalities. In this paper, we present a three-step framework for sparse image matching of SAR and optical imagery, whereby each step is encoded by a deep neural network. We first predict regions in each image which are deemed most suitable for matching. A correspondence heatmap is then generated through a multi-scale, feature-space cross-correlation operator. Finally, outliers are removed by classifying the correspondence surface as a positive or negative match. Our experiments show that the proposed approach provides a substantial improvement over previous methods for SAR-optical image matching and can be used to register even large-scale scenes. This opens up the possibility of using both types of data jointly, for example for the improvement of the geo-localization of optical satellite imagery or multi-sensor stereogrammetry. Numéro de notice : A2020-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.012 Date de publication en ligne : 03/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96062
in ISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 166 - 179[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020113 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020112 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Displacement monitoring of upper Atbara dam based on time series InSAR / Q.Q. Wang in Survey review, vol 52 n° 375 (November 2020)
[article]
Titre : Displacement monitoring of upper Atbara dam based on time series InSAR Type de document : Article/Communication Auteurs : Q.Q. Wang, Auteur ; Q.H. Huang, Auteur ; N. He, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 485 - 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] auscultation d'ouvrage
[Termes IGN] barrage
[Termes IGN] déformation d'édifice
[Termes IGN] érosion hydrique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] série temporelle
[Termes IGN] Soudan
[Termes IGN] surveillance d'ouvrageRésumé : (auteur) Dam is an important part of engineering structure, in the process of dam construction, the dam monitoring is crucial since water erosion and time-dependent motion may cause deformation. Traditional monitoring methods are time-consuming and labour-intensive. However, Interferometric Synthetic Aperture Radar (InSAR) can provide precise and spatially dense information on slow deformations. This research investigated the longest earth-rock-fill dam in Sudan to determine the spatial and temporal deformations Sentinel-1A descending SAR images were further used to analyse the issues mentioned above. The results suggested that the dam existed the maximum displacement with a value up to 190 mm on the dam crest. Besides, the selected sections along the riverbed of the dam were analysed and the RMSE was approximately 2 mm/year. The results were in good agreement with the in situ measurements, indicating the advancement of time series InSAR in dam deformation monitoring. Numéro de notice : A2020-686 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2019.1643529 Date de publication en ligne : 17/07/2019 En ligne : https://doi.org/10.1080/00396265.2019.1643529 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96217
in Survey review > vol 52 n° 375 (November 2020) . - pp 485 - 496[article]Fusion of sparse model based on randomly erased image for SAR occluded target recognition / Zhiqiang He in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : Fusion of sparse model based on randomly erased image for SAR occluded target recognition Type de document : Article/Communication Auteurs : Zhiqiang He, Auteur ; Huaitie Xiao, Auteur ; Chao Gao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7829 - 7844 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] cible cachée
[Termes IGN] détection de cible
[Termes IGN] détection de partie cachée
[Termes IGN] image radar moirée
[Termes IGN] reconstruction d'image
[Termes IGN] représentation parcimonieuseRésumé : (auteur) The recognition of partially occluded targets is a difficult problem in the field of synthetic aperture radar (SAR) target recognition. To eliminate the effect of occlusion, the intuitive idea is to determine the exact location and the size of the occluded area. However, this is very difficult, even impossible in practice. In order to avoid this difficulty and to improve the recognition performance for the partially occluded target, a fusion strategy of the sparse representation (SR) model based on randomly erased images is proposed to recognize the partially occluded target. The proposed method randomly erases some areas many times in both the test samples and the training samples. The erased training samples in each erasure are used to sparsely represent the corresponding erased test sample. Finally, all the SR results are fused to recognize the test sample. The proposed method utilizes random erasure to eliminate the possible occluded region. In addition, this method uses the fusion strategy to overcome under-erasing of the occluded region and erroneous erasure of the unoccluded region. The key parameter of the proposed method is the erasure ratio only. Although the erasure is random, the recognition performance of the method is relatively stable. Therefore, the method can eliminate the influence of occlusion without determining the details of occlusion. The experimental results show that the proposed method is significantly better than the state-of-the-art methods in the case of occlusion. Additionally, the recognition performance of the proposed method is similar to some comparison methods in the case of no occlusion. Numéro de notice : A2020-680 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2984577 Date de publication en ligne : 14/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2984577 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96204
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7829 - 7844[article]A generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery / Niangang Jiao in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)
[article]
Titre : A generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery Type de document : Article/Communication Auteurs : Niangang Jiao, Auteur ; Feng Wang, Auteur ; Hongjian You, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 377 - 388 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] chaîne de traitement
[Termes IGN] correction géométrique
[Termes IGN] étalonnage géométrique
[Termes IGN] géolocalisation
[Termes IGN] image Gaofen
[Termes IGN] image Jilin
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image satellite
[Termes IGN] point d'appui
[Termes IGN] précision géométrique (imagerie)Résumé : (auteur) To date, numerous Earth observation datasets from different types of satellites have been widely used in photogrammetric fields, including urban 3D modelling and geographic information systems. The development of small satellites has provided a new way to obtain repeated observations in a short period. However, compared with that of standard satellite imagery, the geometric performance of imagery from small satellites is relatively poor, restricting their photogrammetric applications. Traditional methods can improve the accuracy of optical images with the addition of well-distributed ground control points (GCPs), which require considerable financial and human resources. The collection of multi-view datasets is an alternative method for geometric processing without GCPs, but relies heavily on the stability and revisit period of satellite platforms. Therefore, this paper presents a framework for improving the geopositioning accuracy of multi-source datasets obtained from optical and synthetic aperture radar (SAR) satellites, and a novel heterogeneous weight strategy is proposed based on an analysis of the geometric error sources of SAR and optical images. The geometric performance of multi-source optical imagery from the Jilin-1 (JL-1) small satellite constellation is evaluated and analysed first, and then Gaofen-3 (GF-3) SAR images are calibrated based on statistical analysis for the production of virtual control points (VCPs). Based on our proposed heterogeneous weight strategy, multi-source optical and SAR images are integrated to improve the geopositioning accuracy. Experimental results indicate that our proposed model can achieve the best performance compared with other popular models, producing an accuracy of approximately 3 m in planimetry and 2 m in height, thereby providing a generic way to synergistically use multi-source remote sensing data. Numéro de notice : A2020-642 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.017 Date de publication en ligne : 12/10/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96066
in ISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 377 - 388[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020113 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020112 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Geostatistical analysis and mitigation of the atmospheric phase screens in Ku-band terrestrial radar interferometric observations of an alpine glacier / Simone Baffelli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : Geostatistical analysis and mitigation of the atmospheric phase screens in Ku-band terrestrial radar interferometric observations of an alpine glacier Type de document : Article/Communication Auteurs : Simone Baffelli, Auteur ; Othmar Frey, Auteur ; Irena Hajnsek, Auteur Année de publication : 2020 Article en page(s) : pp 7533 - 7556 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Alpes
[Termes IGN] analyse spatio-temporelle
[Termes IGN] bande Ku
[Termes IGN] covariance
[Termes IGN] erreur de phase
[Termes IGN] géostatistique
[Termes IGN] glacier
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] série temporelle
[Termes IGN] vapeur d'eau
[Termes IGN] variogrammeRésumé : (auteur) Terrestrial radar interferometry (TRI) can measure displacements at high temporal resolution, potentially with high accuracy. An application of this method is the observation of the surface flow velocity of steep, fast-flowing aglaciers. For these observations, the main factor limiting the accuracy of TRI observations is the spatial and temporal variabilities in the distribution of atmospheric water vapor content, causing a phase delay [atmospheric phase screen (APS)] whose magnitude is similar to the displacement phase. This contribution presents a geostatistical analysis of the spatial and temporal behaviors of the APS in Ku-Band TRI. The analysis is based on the assumption of a separable spatiotemporal covariance structure, which is tested empirically using variogram analysis. From this analysis, spatial and temporal APS statistics are estimated and used in a two-step procedure combining regression-Kriging with generalized least squares (GLS) inversion to estimate a velocity time-series. The performance of this method is evaluated by cross-validation using phase observations on stable scatterers. This analysis shows a considerable reduction in residual phase variance compared with the standard approach of combining the linear models of APS stratification and interferogram stacking. Numéro de notice : A2020-675 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976656 Date de publication en ligne : 13/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96166
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7533 - 7556[article]Textural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])PermalinkTime series potential assessment for biophysical characterization of orchards and crops in a mixed scenario with Sentinel-1A SAR data / Hemant Sahu in Geocarto international, vol 35 n° 14 ([15/10/2020])PermalinkApplication of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data / Mahdi Moalla in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkCombined InSAR and terrestrial structural monitoring of bridges / Sivasakthy Selvakumaran in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkMapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)PermalinkBackground tropospheric delay in geosynchronous synthetic aperture radar / Dexin Li in Remote sensing, vol 12 n° 18 (September-2 2020)PermalinkL-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia / Bambang H Trisasongko in Geocarto international, vol 35 n° 12 ([01/09/2020])PermalinkDeriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 / Helena Bergstedt in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkIlluminating the spatio-temporal evolution of the 2008–2009 Qaidam earthquake sequence with the joint use of Insar time series and teleseismic data / Simon Daout in Remote sensing, vol 12 n° 17 (September-1 2020)PermalinkMultiscale supervised kernel dictionary learning for SAR target recognition / Lei Tao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkA novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkShip detection in SAR images via local contrast of Fisher vectors / Xueqian Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkA spaceborne SAR-based procedure to support the detection of landslides / Giuseppe Esposito in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)PermalinkX-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkConjugate ruptures and seismotectonic implications of the 2019 Mindanao earthquake sequence inferred from Sentinel-1 InSAR data / Bingquan Li in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)PermalinkA novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images / David Pirrone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkPolarimetric SAR calibration and residual error estimation when corner reflectors are unavailable / Lei Shi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkPotential of texture from SAR tomographic images for forest aboveground biomass estimation / Zhanmang Liao in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)PermalinkSubpixel SAR image registration through parabolic interpolation of the 2-D cross correlation / Luca Pallotta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkUnsupervised change detection between SAR images based on hypergraphs / Jun Wang in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)Permalink