E
Emmanuel Trouvé
Researcher at University of Savoy
Publications - 146
Citations - 2231
Emmanuel Trouvé is an academic researcher from University of Savoy. The author has contributed to research in topics: Synthetic aperture radar & Glacier. The author has an hindex of 19, co-authored 134 publications receiving 1803 citations. Previous affiliations of Emmanuel Trouvé include École Normale Supérieure.
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Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation
TL;DR: Subjective and objective performance analysis, including coherence edge detection, receiver operating characteristics plots, and bias reduction tables, recommends the proposed algorithm as an effective POL-InSAR postprocessing technique.
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Twenty-first century glacier slowdown driven by mass loss in High Mountain Asia
Amaury Dehecq,Amaury Dehecq,Amaury Dehecq,Noel Gourmelen,Noel Gourmelen,Alex S. Gardner,Fanny Brun,Fanny Brun,Daniel Goldberg,Peter Nienow,Etienne Berthier,C. Vincent,Patrick Wagnon,Emmanuel Trouvé +13 more
TL;DR: In this article, the authors present observations of changes in ice flow for all glaciers in High Mountain Asia over the period 2000-2017, based on one million pairs of optical satellite images.
Twenty-first Century Glacier Slowdown Driven By Mass Loss In High Mountain Asia
Amaury Dehecq,Noel Gourmelen,Noel Gourmelen,Alex S. Gardner,Fanny Brun,Daniel Goldberg,Peter Nienow,Etienne Berthier,C. Vincent,Patrick Wagnon,Emmanuel Trouvé +10 more
TL;DR: In this article, the authors present observations of changes in ice flow for all glaciers in High Mountain Asia over the period 2000-2017, based on one million pairs of optical satellite images.
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Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir–Karakoram–Himalaya
TL;DR: In this article, a semi-automated approach was proposed to derive robust and spatially complete glacier velocities and their uncertainties on a large spatial scale using complete satellite image feature tracking.
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Unsupervised Spatiotemporal Mining of Satellite Image Time Series Using Grouped Frequent Sequential Patterns
Andreea Julea,Nicolas Méger,Philippe Bolon,Christophe Rigotti,Marie-Pierre Doin,Cécile Lasserre,Emmanuel Trouvé,Vasile Lazarescu +7 more
TL;DR: To manage the huge amount of data and the large number of potential temporal evolutions, a new approach based on data-mining techniques is presented and a frequent sequential pattern extraction method adapted to that spatiotemporal context is developed.