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

被引：82

作者：

Campos-Taberner, Manuel ^{[1
]}

Romero-Soriano, Adriana ^{[2
]}

Gatta, Carlo ^{[3
]}

Camps-Valls, Gustau ^{[1
]}

Lagrange, Adrien ^{[4
]}

Le Saux, Bertrand ^{[4
]}

Beaupere, Anne ^{[4
]}

Boulch, Alexandre ^{[4
]}

Chan-Hon-Tong, Adrien ^{[4
]}

Herbin, Stephane ^{[4
]}

Randrianarivo, Hicham ^{[4
]}

Ferecatu, Marin ^{[5
]}

Shimoni, Michal ^{[6
]}

Moser, Gabriele ^{[7
]}

Tuia, Devis ^{[8
]}

机构：

[1] Univ Valencia, E-46100 Valencia, Spain

[2] Univ Barcelona, E-08007 Barcelona, Spain

[3] Univ Autonoma Barcelona, E-08193 Barcelona, Spain

[4] French Aerosp Lab, Off Natl Etud Rech Aerospatiales, F-91123 Palaiseau, France

[5] Conservatoire Natl Arts & Metiers Cedr, F-75141 Paris, France

[6] Royal Mil Acad, Signal & Image Ctr, Dept Elect Engn, B-1000 Brussels, Belgium

[7] Univ Genoa, Dept Elect Elect Telecommun Engn & Naval Architec, I-16145 Genoa, Italy

[8] Univ Zurich, Dept Geog, CH-8057 Zurich, Switzerland

来源：

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | 2016年 / 9卷 / 12期

基金：

欧洲研究理事会; 瑞士国家科学基金会;

关键词：

Deep neural networks; extremely high spatial resolution; image analysis and data fusion (IADF); landcover classification; LiDAR; multiresolution-; multisource-; multimodal-data fusion; HYPERSPECTRAL IMAGES; CLASSIFICATION;

D O I：

10.1109/JSTARS.2016.2569162

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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/technicalcommittees/data-fusion/2015-ieee-grss-data-fusion-contest/. The 3-D part of the contest is discussed in the Part-B paper [1].

引用

页码：5547 / 5559

页数：13

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