6533b873fe1ef96bd12d4e70
RESEARCH PRODUCT
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
Devis TuiaBertrand Le SauxHicham RandrianarivoAdriana Romero-sorianoMarin FerecatuAdrien LagrangeStéphane HerbinAnne BeaupereGabriele MoserAdrien Chan-hon-tongMichal ShimoniGustau Camps-vallsAlexandre BoulchCarlo GattaManuel Campos-tabernersubject
Atmospheric Science010504 meteorology & atmospheric sciencesComputer scienceMULTIMODAL-DATA FUSIONGeophysics. Cosmic physics0211 other engineering and technologies02 engineering and technologyCONTESTcomputer.software_genre01 natural sciencesOutcome (game theory)LIDARTraitement des imagesIMAGE ANALYSIS AND DATA FUSION (IADF)DEEP NEURAL NETWORKSDeep neural networksTraitement du signal et de l'imageMULTIRESOLUTION910 Geography & travelMultiresolutionGround truthLANDCOVER CLASSIFICATIONIMAGE AERIENNE1903 Computers in Earth SciencesBenchmarkingVision par ordinateur et reconnaissance de formesOcean engineering10122 Institute of GeographyLidarDeep neural networksData miningExtremely high spatial resolutionMultimodal-data fusionLiDARComputers in Earth Sciences; Atmospheric ScienceImage analysis and data fusion (IADF)EXTREMELY HIGH SPATIAL RESOLUTIONCLASSIFICATIONTRAITEMENT IMAGE1902 Atmospheric ScienceAPPRENTISSAGE STATISTIQUEComputers in Earth SciencesTELEDETECTIONSynthèse d'image et réalité virtuelleTC1501-1800021101 geological & geomatics engineering0105 earth and related environmental sciencesLandcover classificationmultiresolution-[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]QC801-809Intelligence artificielleMULTISOURCESensor fusionRGB color modelcomputerMultisourcedescription
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].
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-08-08 |