TY - JOUR
T1 - Processing of Extremely High-Resolution LiDAR and RGB Data
T2 - Outcome of the 2015 IEEE GRSS Data Fusion Contest-Part A: 2-D Contest
AU - Campos-Taberner, Manuel
AU - Romero-Soriano, Adriana
AU - Gatta, Carlo
AU - Camps-Valls, Gustau
AU - Lagrange, Adrien
AU - Le Saux, Bertrand
AU - Beaupere, Anne
AU - Boulch, Alexandre
AU - Chan-Hon-Tong, Adrien
AU - Herbin, Stephane
AU - Randrianarivo, Hicham
AU - Ferecatu, Marin
AU - Shimoni, Michal
AU - Moser, Gabriele
AU - Tuia, Devis
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2016/12
Y1 - 2016/12
N2 - 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].
AB - 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].
KW - Deep neural networks
KW - LiDAR
KW - extremely high spatial resolution
KW - image analysis and data fusion (IADF)
KW - landcover classification
KW - multimodal-data fusion
KW - multiresolution-
KW - multisource-
UR - https://www.scopus.com/pages/publications/85026995922
U2 - 10.1109/JSTARS.2016.2569162
DO - 10.1109/JSTARS.2016.2569162
M3 - Article
AN - SCOPUS:85026995922
SN - 1939-1404
VL - 9
SP - 5547
EP - 5559
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IS - 12
ER -