S
Sylvie Le Hégarat-Mascle
Researcher at Université Paris-Saclay
Publications - 59
Citations - 684
Sylvie Le Hégarat-Mascle is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: GNSS applications & Markov random field. The author has an hindex of 13, co-authored 59 publications receiving 594 citations. Previous affiliations of Sylvie Le Hégarat-Mascle include University of Paris & Centre national de la recherche scientifique.
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Use of Markov Random Fields for automatic cloud/shadow detection on high resolution optical images
TL;DR: An automatic detection algorithm for cloud/shadow on remote sensing optical images based on physical properties of clouds and shadows, namely for a cloud and its associated shadow, which is formalized using Markov Random Field (MRF) framework at two levels.
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Surface and aerodynamic roughness in arid and semiarid areas and their relation to radar backscatter coefficient
Béatrice Marticorena,M. Kardous,Gilles Bergametti,Yann Callot,Yann Callot,Patrick Chazette,Houcine Khatteli,Sylvie Le Hégarat-Mascle,M. Maille,Jean-Louis Rajot,D. Vidal-Madjar,Mehrez Zribi +11 more
TL;DR: In this paper, an intensive field campaign was performed in southern Tunisia to measure the lateral cover, Lc, and theaerodynamic roughness length, Z0, over 10 sites with different surface roughnesses.
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Determination of vegetation cover fraction by inversion of a four-parameter model based on isoline parametrization
TL;DR: In this paper, the authors focused on the determination of the fraction of vegetation cover (fCover) based on the inversion of a four-parameter model combining the reflectances in the Red (R) and Near Infrared (NIR) domains.
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Evidential query-by-committee active learning for pedestrian detection in high-density crowds
TL;DR: The results show that the proposed fusion algorithm is effective in exploiting the strengths of the individual classifiers, as well as in augmenting the training set with informative samples which allow the resulting detector to enhance its performance.
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Application of ant colony optimization to adaptive routing in aleo telecomunications satellite network
TL;DR: The best Aco variant consistently gives performance superior to that obtained with a standard link state algorithm (Spf), under a variety of traffic conditions, and at negligible cost in terms of routing bandwidth.