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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])
[article]
Titre : Textural classification of remotely sensed images using multiresolution techniques Type de document : Article/Communication Auteurs : Rizwan Ahmed Ansari, Auteur ; Krishna Mohan Buddhiraju, Auteur ; Avik Bhattacharya, Auteur Année de publication : 2020 Article en page(s) : pp 1580 - 1602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse multirésolution
[Termes IGN] analyse texturale
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de contours
[Termes IGN] distance euclidienne
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image RVB
[Termes IGN] image satellite
[Termes IGN] texture d'image
[Termes IGN] transformation en ondelettesRésumé : (auteur) Multiresolution analysis (MRA) methods have been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a methodology to identify different classes in satellite images using texture features from newly developed multiresolution methods. The proposed method is tested on remotely sensed optical images and a Pauli RGB decomposed version of synthetic aperture radar image. The textural information is extracted at various scales and in different directions from curvelet and contourlet transforms. The results are compared with wavelet-based features. Accuracy assessment is performed and comparative analysis is carried out using minimum distance to mean, support vector machine and random forest classifiers. It is found that the proposed method shows better class discriminating power and classification capability as compared to existing wavelet-based method. Numéro de notice : A2020-618 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581263 Date de publication en ligne : 15/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95994
in Geocarto international > vol 35 n° 14 [15/10/2020] . - pp 1580 - 1602[article]Time 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])
[article]
Titre : Time series potential assessment for biophysical characterization of orchards and crops in a mixed scenario with Sentinel-1A SAR data Type de document : Article/Communication Auteurs : Hemant Sahu, Auteur ; Dipanwita Haldar, Auteur ; Abhishek Danodia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1627 - 1639 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] modèle de rétrodiffusion
[Termes IGN] polarisation
[Termes IGN] série temporelle
[Termes IGN] variable biophysique (végétation)
[Termes IGN] vergerRésumé : (auteur) Potential of Sentinel-1A SAR data was assessed for the time-series analysis of orchard biophysical parameters and crop system. The study revealed characteristics variations in the backscatter coefficient with respect to time and polarization for age in VH polarization than in VV and ratio of VV/VH polarization showing discrimination of young orchard particularly in VV polarization. The parameter of the orchard (age, DBH, canopy radius and visual height) shows a promising relationship with backscatter coefficient. Out of several regression models, VV channel responds with a fair regression coefficient of 0.54, 0.52, 0.48 and 0.44 for height with rmse of 0.5, 1.3, 0.7 and 0.6 for age, DBH, canopy radius and visual height, respectively. Multiple regression coefficient of 0.61 was observed for January 2018 in VV polarization as best date for study. These empirical relationships have potential for the inverse backscatter modelling. Numéro de notice : A2020-620 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1583776 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1583776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96003
in Geocarto international > vol 35 n° 14 [15/10/2020] . - pp 1627 - 1639[article]Application 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)
[article]
Titre : Application of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data Type de document : Article/Communication Auteurs : Mahdi Moalla, Auteur ; Hichem Frigui, Auteur ; Andrew Karem, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7022 - 7034 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] cible cachée
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données radar
[Termes IGN] image radar moirée
[Termes IGN] mine antipersonnel
[Termes IGN] radar pénétrant GPR
[Termes IGN] réseau neuronal récurrent
[Termes IGN] sous-solRésumé : (auteur) We propose discrimination algorithms for buried threat detection (BTD) that exploit deep convolutional neural networks (CNNs) and recurrent neural networks (RNN) to analyze 2-D GPR B-scans in the down-track (DT) and cross-track (CT) directions as well as 3-D GPR volumes. Instead of imposing a specific model or handcrafted features, as in most existing detectors, we use large real GPR data collections and data-driven approaches that learn: 1) features characterizing buried explosive objects (BEOs) in 2-D B-scans, both in the DT and CT directions; 2) the variation of the CNN features learned in a fixed 2-D view across the third dimension; and 3) features characterizing BEOs in the original 3-D space. The proposed algorithms were trained and evaluated using large experimental GPR data covering a surface area of 120 000 m 2 from 13 different lanes across two U.S. test sites. These data include a diverse set of BEOs consisting of varying shapes, metal content, and underground burial depths. We provide some qualitative analysis of the proposed algorithms by visually comparing their performance and consistency along different dimensions and visualizing typical features learned by some nodes of the network. We also provide quantitative analysis that compares the receiver operating characteristics (ROCs) obtained using the proposed algorithms with those obtained using existing approaches based on CNN as well as traditional learning. Numéro de notice : A2020-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2978763 Date de publication en ligne : 25/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2978763 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95914
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 7022 - 7034[article]Combined InSAR and terrestrial structural monitoring of bridges / Sivasakthy Selvakumaran in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
[article]
Titre : Combined InSAR and terrestrial structural monitoring of bridges Type de document : Article/Communication Auteurs : Sivasakthy Selvakumaran, Auteur ; Cristian Rossi, Auteur ; Andrea Marinoni, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7141 - 7153 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] coin réflecteur
[Termes IGN] données multisources
[Termes IGN] image radar moirée
[Termes IGN] incertitude des données
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Londres
[Termes IGN] pont
[Termes IGN] surveillance d'ouvrage
[Termes IGN] tachéomètre électroniqueRésumé : (auteur) This article examines advances in interferometric synthetic aperture radar (InSAR) satellite measurement technologies to understand their relevance, utilization, and limitations for bridge monitoring. Waterloo Bridge is presented as a case study to explore how InSAR data sets can be combined with traditional measurement techniques including sensors installed on the bridge and automated total stations. A novel approach to InSAR bridge monitoring was adopted by the installation of physical reflectors at key points of structural interest on the bridge, in order to supplement the bridge’s own reflection characteristics and ensure that the InSAR measurements could be directly compared and combined with in situ measurements. The interpretation and integration of InSAR data sets with civil infrastructure data are more than a trivial task, and a discussion of uncertainty of measurement data is presented. Finally, a strategy for combining and interpreting varied data from multiple sources to provide useful insights into each of these methods is presented, outlining the practical applications of this data analysis to support wider monitoring strategies. Numéro de notice : A2020-588 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2979961 Date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2979961 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95916
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 7141 - 7153[article]Mapping 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)PermalinkWide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 / Dirk Hoekman in Remote sensing, vol 12 n° 19 (October-1 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)Permalink