Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest
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...
Ausführliche Beschreibung
Autor*in: |
Manuel Campos-Taberner [verfasserIn] Adriana Romero-Soriano [verfasserIn] Carlo Gatta [verfasserIn] Gustau Camps-Valls [verfasserIn] Adrien Lagrange [verfasserIn] Bertrand Le Saux [verfasserIn] Anne Beaupere [verfasserIn] Alexandre Boulch [verfasserIn] Adrien Chan-Hon-Tong [verfasserIn] Stephane Herbin [verfasserIn] Hicham Randrianarivo [verfasserIn] Marin Ferecatu [verfasserIn] Michal Shimoni [verfasserIn] Gabriele Moser [verfasserIn] Devis Tuia [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Schlagwörter: |
extremely high spatial resolution |
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Übergeordnetes Werk: |
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 9(2016), 12, Seite 5547-5559 |
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Übergeordnetes Werk: |
volume:9 ; year:2016 ; number:12 ; pages:5547-5559 |
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DOI / URN: |
10.1109/JSTARS.2016.2569162 |
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Katalog-ID: |
DOAJ002127431 |
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10.1109/JSTARS.2016.2569162 doi (DE-627)DOAJ002127431 (DE-599)DOAJbfa02af46c654043b1022ae06944806b DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Manuel Campos-Taberner verfasserin aut Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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]. Deep neural networks extremely high spatial resolution image analysis and data fusion (IADF) landcover classification LiDAR multiresolution- Ocean engineering Geophysics. Cosmic physics Adriana Romero-Soriano verfasserin aut Carlo Gatta verfasserin aut Gustau Camps-Valls verfasserin aut Adrien Lagrange verfasserin aut Bertrand Le Saux verfasserin aut Anne Beaupere verfasserin aut Alexandre Boulch verfasserin aut Adrien Chan-Hon-Tong verfasserin aut Stephane Herbin verfasserin aut Hicham Randrianarivo verfasserin aut Marin Ferecatu verfasserin aut Michal Shimoni verfasserin aut Gabriele Moser verfasserin aut Devis Tuia verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 9(2016), 12, Seite 5547-5559 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:9 year:2016 number:12 pages:5547-5559 https://doi.org/10.1109/JSTARS.2016.2569162 kostenfrei https://doaj.org/article/bfa02af46c654043b1022ae06944806b kostenfrei https://ieeexplore.ieee.org/document/7536139/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 9 2016 12 5547-5559 |
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10.1109/JSTARS.2016.2569162 doi (DE-627)DOAJ002127431 (DE-599)DOAJbfa02af46c654043b1022ae06944806b DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Manuel Campos-Taberner verfasserin aut Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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]. Deep neural networks extremely high spatial resolution image analysis and data fusion (IADF) landcover classification LiDAR multiresolution- Ocean engineering Geophysics. Cosmic physics Adriana Romero-Soriano verfasserin aut Carlo Gatta verfasserin aut Gustau Camps-Valls verfasserin aut Adrien Lagrange verfasserin aut Bertrand Le Saux verfasserin aut Anne Beaupere verfasserin aut Alexandre Boulch verfasserin aut Adrien Chan-Hon-Tong verfasserin aut Stephane Herbin verfasserin aut Hicham Randrianarivo verfasserin aut Marin Ferecatu verfasserin aut Michal Shimoni verfasserin aut Gabriele Moser verfasserin aut Devis Tuia verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 9(2016), 12, Seite 5547-5559 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:9 year:2016 number:12 pages:5547-5559 https://doi.org/10.1109/JSTARS.2016.2569162 kostenfrei https://doaj.org/article/bfa02af46c654043b1022ae06944806b kostenfrei https://ieeexplore.ieee.org/document/7536139/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 9 2016 12 5547-5559 |
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10.1109/JSTARS.2016.2569162 doi (DE-627)DOAJ002127431 (DE-599)DOAJbfa02af46c654043b1022ae06944806b DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Manuel Campos-Taberner verfasserin aut Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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]. Deep neural networks extremely high spatial resolution image analysis and data fusion (IADF) landcover classification LiDAR multiresolution- Ocean engineering Geophysics. Cosmic physics Adriana Romero-Soriano verfasserin aut Carlo Gatta verfasserin aut Gustau Camps-Valls verfasserin aut Adrien Lagrange verfasserin aut Bertrand Le Saux verfasserin aut Anne Beaupere verfasserin aut Alexandre Boulch verfasserin aut Adrien Chan-Hon-Tong verfasserin aut Stephane Herbin verfasserin aut Hicham Randrianarivo verfasserin aut Marin Ferecatu verfasserin aut Michal Shimoni verfasserin aut Gabriele Moser verfasserin aut Devis Tuia verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 9(2016), 12, Seite 5547-5559 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:9 year:2016 number:12 pages:5547-5559 https://doi.org/10.1109/JSTARS.2016.2569162 kostenfrei https://doaj.org/article/bfa02af46c654043b1022ae06944806b kostenfrei https://ieeexplore.ieee.org/document/7536139/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 9 2016 12 5547-5559 |
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10.1109/JSTARS.2016.2569162 doi (DE-627)DOAJ002127431 (DE-599)DOAJbfa02af46c654043b1022ae06944806b DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Manuel Campos-Taberner verfasserin aut Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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]. Deep neural networks extremely high spatial resolution image analysis and data fusion (IADF) landcover classification LiDAR multiresolution- Ocean engineering Geophysics. Cosmic physics Adriana Romero-Soriano verfasserin aut Carlo Gatta verfasserin aut Gustau Camps-Valls verfasserin aut Adrien Lagrange verfasserin aut Bertrand Le Saux verfasserin aut Anne Beaupere verfasserin aut Alexandre Boulch verfasserin aut Adrien Chan-Hon-Tong verfasserin aut Stephane Herbin verfasserin aut Hicham Randrianarivo verfasserin aut Marin Ferecatu verfasserin aut Michal Shimoni verfasserin aut Gabriele Moser verfasserin aut Devis Tuia verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 9(2016), 12, Seite 5547-5559 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:9 year:2016 number:12 pages:5547-5559 https://doi.org/10.1109/JSTARS.2016.2569162 kostenfrei https://doaj.org/article/bfa02af46c654043b1022ae06944806b kostenfrei https://ieeexplore.ieee.org/document/7536139/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 9 2016 12 5547-5559 |
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10.1109/JSTARS.2016.2569162 doi (DE-627)DOAJ002127431 (DE-599)DOAJbfa02af46c654043b1022ae06944806b DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Manuel Campos-Taberner verfasserin aut Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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]. Deep neural networks extremely high spatial resolution image analysis and data fusion (IADF) landcover classification LiDAR multiresolution- Ocean engineering Geophysics. Cosmic physics Adriana Romero-Soriano verfasserin aut Carlo Gatta verfasserin aut Gustau Camps-Valls verfasserin aut Adrien Lagrange verfasserin aut Bertrand Le Saux verfasserin aut Anne Beaupere verfasserin aut Alexandre Boulch verfasserin aut Adrien Chan-Hon-Tong verfasserin aut Stephane Herbin verfasserin aut Hicham Randrianarivo verfasserin aut Marin Ferecatu verfasserin aut Michal Shimoni verfasserin aut Gabriele Moser verfasserin aut Devis Tuia verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 9(2016), 12, Seite 5547-5559 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:9 year:2016 number:12 pages:5547-5559 https://doi.org/10.1109/JSTARS.2016.2569162 kostenfrei https://doaj.org/article/bfa02af46c654043b1022ae06944806b kostenfrei https://ieeexplore.ieee.org/document/7536139/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_161 GBV_ILN_187 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4700 AR 9 2016 12 5547-5559 |
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TC1501-1800 QC801-809 Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest Deep neural networks extremely high spatial resolution image analysis and data fusion (IADF) landcover classification LiDAR multiresolution- |
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Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest |
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Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest |
abstract |
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]. |
abstractGer |
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]. |
abstract_unstemmed |
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]. |
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Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest |
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