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[LRF16] Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest
Revue Internationale avec comité de lecture : Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IEEE JSTARS), vol. PP(99), pp. -, 2016, (doi:-)Mots clés: Deep neural networks, extremely high spatial resolution, image analysis and data fusion (IADF), landcover classification, LiDAR, multiresolution-, multisource-, multimodal- data fusion.
Résumé:
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 edi-
tion 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 comple-
mentarity of RGB and LiDAR with deep neural networks (winning
team) or provided a comprehensive benchmarking evaluation of
new classification strategies for extremely high-resolution multi-
modal data (runner-up team). The data and the previously undis-
closed 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/.
Collaboration:
ONERA - DTIM
BibTeX
| @article { | |||
| LRF16, | |||
| title | = | "{Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest}", | |
| author | = | "B. Le Saux and H. Randrianarivo and M. Ferecatu", | |
| journal | = | " IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IEEE JSTARS)", | |
| year | = | 2016, | |
| volume | = | PP, | |
| number | = | 99, | |
| pages | = | "-", | |
| doi | = | "-", | |
| } | |||
