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Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest-Part A: 2-D Contest

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Campos-Taberner Manuel, Romero-Soriano Adriana, Gatta Carlo, Camps-Valls Gustau, Lagrange Adrien, Le Saux Bertrand, Beaupere Anne, Boulch Alexandre, Chan-Hon-Tong Adrien, Herbin Stephane, Randrianarivo Hicham, Ferecatu Marin, Shimoni Michal, Moser Gabriele, Tuia Devis,
Project Multimodal machine learning for remote sensing information fusion
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Original article (peer-reviewed)

Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume (Issue) 9(12)
Page(s) 5547 - 5559
Title of proceedings IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
DOI 10.1109/jstars.2016.2569162

Open Access

URL http://ieeexplore.ieee.org/document/7536139/
Type of Open Access Publisher (Gold Open Access)

Abstract

© 2008-2012 IEEE. In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the scientific results obtained by the winners of the 2-D contest, which studied either the complementarity of RGB and LiDAR with deep neural networks (winning team) or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data (runner-up team). The data and the previously undisclosed ground truth will remain available for the community and can be obtained at http://www.grss-ieee.org/community/technical-committees/data-fusion/2015-ieee-grss-data-fusion-contest/. The 3-D part of the contest is discussed in the Part-B paper [1].
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