Traitement de données RGB et Lidar à extrêmement haute résolution: retombées de la compétition de fusion de données 2015 de l'IEEE GRSS - Partie A / compétition 2D
| Autor: | Devis Tuia, Bertrand Le Saux, Hicham Randrianarivo, Adriana Romero-Soriano, Marin Ferecatu, Adrien Lagrange, Stéphane Herbin, Anne Beaupere, Gabriele Moser, Adrien Chan-Hon-Tong, Michal Shimoni, Gustau Camps-Valls, Alexandre Boulch, Carlo Gatta, Manuel Campos-Taberner |
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| Přispěvatelé: | Universitat de València (UV), Universitat de Barcelona (UB), Universitat Autònoma de Barcelona (UAB), ONERA - The French Aerospace Lab [Palaiseau], ONERA-Université Paris Saclay (COmUE), École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Royal Military Academy (RMA), University of Genoa (UNIGE), Universität Zürich [Zürich] = University of Zurich (UZH), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), University of Zurich, Tuia, Devis |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Computer science MULTIMODAL-DATA FUSION Geophysics. Cosmic physics 0211 other engineering and technologies 02 engineering and technology CONTEST computer.software_genre 01 natural sciences Outcome (game theory) LIDAR Traitement des images IMAGE ANALYSIS AND DATA FUSION (IADF) DEEP NEURAL NETWORKS Deep neural networks Traitement du signal et de l'image MULTIRESOLUTION 910 Geography & travel Multiresolution Ground truth LANDCOVER CLASSIFICATION IMAGE AERIENNE 1903 Computers in Earth Sciences Benchmarking Vision par ordinateur et reconnaissance de formes Ocean engineering 10122 Institute of Geography Lidar Data mining Extremely high spatial resolution Multimodal-data fusion LiDAR Computers in Earth Sciences Image analysis and data fusion (IADF) EXTREMELY HIGH SPATIAL RESOLUTION CLASSIFICATION TRAITEMENT IMAGE 1902 Atmospheric Science APPRENTISSAGE STATISTIQUE TELEDETECTION Synthèse d'image et réalité virtuelle TC1501-1800 021101 geological & geomatics engineering 0105 earth and related environmental sciences Landcover classification multiresolution [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] QC801-809 Intelligence artificielle MULTISOURCE Sensor fusion RGB color model computer Multisource |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2016, 9 (12), p. 5547-5559. ⟨10.1109/JSTARS.2016.2569162⟩ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 9, Iss 12, Pp 5547-5559 (2016) |
| ISSN: | 1939-1404 |
| DOI: | 10.1109/JSTARS.2016.2569162⟩ |
| Popis: | International audience; 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]. |
| Databáze: | OpenAIRE |
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