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

Gespeichert in:

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]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Deep neural networks extremely high spatial resolution image analysis and data fusion (IADF) landcover classification LiDAR multiresolution-

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 9(2016), 12, Seite 5547-5559
Übergeordnetes Werk:	volume:9 ; year:2016 ; number:12 ; pages:5547-5559

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2016.2569162

Katalog-ID:	DOAJ002127431

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ002127431
003	DE-627
005	20230503004027.0
007	cr uuu---uuuuu
008	230225s2016 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/JSTARS.2016.2569162 \|2 doi
035			\|a (DE-627)DOAJ002127431
035			\|a (DE-599)DOAJbfa02af46c654043b1022ae06944806b
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TC1501-1800
050		0	\|a QC801-809
100	0		\|a Manuel Campos-Taberner \|e verfasserin \|4 aut
245	1	0	\|a Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest
264		1	\|c 2016
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a 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].
650		4	\|a Deep neural networks
650		4	\|a extremely high spatial resolution
650		4	\|a image analysis and data fusion (IADF)
650		4	\|a landcover classification
650		4	\|a LiDAR
650		4	\|a multiresolution-
653		0	\|a Ocean engineering
653		0	\|a Geophysics. Cosmic physics
700	0		\|a Adriana Romero-Soriano \|e verfasserin \|4 aut
700	0		\|a Carlo Gatta \|e verfasserin \|4 aut
700	0		\|a Gustau Camps-Valls \|e verfasserin \|4 aut
700	0		\|a Adrien Lagrange \|e verfasserin \|4 aut
700	0		\|a Bertrand Le Saux \|e verfasserin \|4 aut
700	0		\|a Anne Beaupere \|e verfasserin \|4 aut
700	0		\|a Alexandre Boulch \|e verfasserin \|4 aut
700	0		\|a Adrien Chan-Hon-Tong \|e verfasserin \|4 aut
700	0		\|a Stephane Herbin \|e verfasserin \|4 aut
700	0		\|a Hicham Randrianarivo \|e verfasserin \|4 aut
700	0		\|a Marin Ferecatu \|e verfasserin \|4 aut
700	0		\|a Michal Shimoni \|e verfasserin \|4 aut
700	0		\|a Gabriele Moser \|e verfasserin \|4 aut
700	0		\|a Devis Tuia \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing \|d IEEE, 2020 \|g 9(2016), 12, Seite 5547-5559 \|w (DE-627)581732634 \|w (DE-600)2457423-5 \|x 21511535 \|7 nnns
773	1	8	\|g volume:9 \|g year:2016 \|g number:12 \|g pages:5547-5559
856	4	0	\|u https://doi.org/10.1109/JSTARS.2016.2569162 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/bfa02af46c654043b1022ae06944806b \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/7536139/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2151-1535 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_70
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_120
912			\|a GBV_ILN_161
912			\|a GBV_ILN_187
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2965
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4328
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 9 \|j 2016 \|e 12 \|h 5547-5559

Indexfelder

author_variant	m c t mct a r s ars c g cg g c v gcv a l al b l s bls a b ab a b ab a c h ach s h sh h r hr m f mf m s ms g m gm d t dt
matchkey_str	article:21511535:2016----::rcsigfxrmlhgrsltoldrnrbaaucmote05eersaa
hierarchy_sort_str	2016
callnumber-subject-code	TC
publishDate	2016
allfields	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
spelling	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
allfields_unstemmed	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
allfieldsGer	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
allfieldsSound	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
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(2016), 12, Seite 5547-5559 volume:9 year:2016 number:12 pages:5547-5559
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(2016), 12, Seite 5547-5559 volume:9 year:2016 number:12 pages:5547-5559
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Deep neural networks extremely high spatial resolution image analysis and data fusion (IADF) landcover classification LiDAR multiresolution- Ocean engineering Geophysics. Cosmic physics
isfreeaccess_bool	true
container_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
authorswithroles_txt_mv	Manuel Campos-Taberner @@aut@@ Adriana Romero-Soriano @@aut@@ Carlo Gatta @@aut@@ Gustau Camps-Valls @@aut@@ Adrien Lagrange @@aut@@ Bertrand Le Saux @@aut@@ Anne Beaupere @@aut@@ Alexandre Boulch @@aut@@ Adrien Chan-Hon-Tong @@aut@@ Stephane Herbin @@aut@@ Hicham Randrianarivo @@aut@@ Marin Ferecatu @@aut@@ Michal Shimoni @@aut@@ Gabriele Moser @@aut@@ Devis Tuia @@aut@@
publishDateDaySort_date	2016-01-01T00:00:00Z
hierarchy_top_id	581732634
id	DOAJ002127431
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ002127431</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503004027.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2016 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/JSTARS.2016.2569162</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ002127431</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJbfa02af46c654043b1022ae06944806b</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TC1501-1800</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC801-809</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Manuel Campos-Taberner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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].</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">extremely high spatial resolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image analysis and data fusion (IADF)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">landcover classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">LiDAR</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multiresolution-</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Ocean engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geophysics. Cosmic physics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Adriana Romero-Soriano</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Carlo Gatta</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gustau Camps-Valls</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Adrien Lagrange</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bertrand Le Saux</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anne Beaupere</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Alexandre Boulch</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Adrien Chan-Hon-Tong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Stephane Herbin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hicham Randrianarivo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Marin Ferecatu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Michal Shimoni</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gabriele Moser</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Devis Tuia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</subfield><subfield code="d">IEEE, 2020</subfield><subfield code="g">9(2016), 12, Seite 5547-5559</subfield><subfield code="w">(DE-627)581732634</subfield><subfield code="w">(DE-600)2457423-5</subfield><subfield code="x">21511535</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:12</subfield><subfield code="g">pages:5547-5559</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/JSTARS.2016.2569162</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/bfa02af46c654043b1022ae06944806b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/7536139/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2151-1535</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2965</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2016</subfield><subfield code="e">12</subfield><subfield code="h">5547-5559</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Manuel Campos-Taberner
spellingShingle	Manuel Campos-Taberner misc TC1501-1800 misc QC801-809 misc Deep neural networks misc extremely high spatial resolution misc image analysis and data fusion (IADF) misc landcover classification misc LiDAR misc multiresolution- misc Ocean engineering misc Geophysics. Cosmic physics Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest
authorStr	Manuel Campos-Taberner
ppnlink_with_tag_str_mv	@@773@@(DE-627)581732634
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TC1501-1800
illustrated	Not Illustrated
issn	21511535
topic_title	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-
topic	misc TC1501-1800 misc QC801-809 misc Deep neural networks misc extremely high spatial resolution misc image analysis and data fusion (IADF) misc landcover classification misc LiDAR misc multiresolution- misc Ocean engineering misc Geophysics. Cosmic physics
topic_unstemmed	misc TC1501-1800 misc QC801-809 misc Deep neural networks misc extremely high spatial resolution misc image analysis and data fusion (IADF) misc landcover classification misc LiDAR misc multiresolution- misc Ocean engineering misc Geophysics. Cosmic physics
topic_browse	misc TC1501-1800 misc QC801-809 misc Deep neural networks misc extremely high spatial resolution misc image analysis and data fusion (IADF) misc landcover classification misc LiDAR misc multiresolution- misc Ocean engineering misc Geophysics. Cosmic physics
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
hierarchy_parent_id	581732634
hierarchy_top_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)581732634 (DE-600)2457423-5
title	Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest
ctrlnum	(DE-627)DOAJ002127431 (DE-599)DOAJbfa02af46c654043b1022ae06944806b
title_full	Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest
author_sort	Manuel Campos-Taberner
journal	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
journalStr	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2016
contenttype_str_mv	txt
container_start_page	5547
author_browse	Manuel Campos-Taberner Adriana Romero-Soriano Carlo Gatta Gustau Camps-Valls Adrien Lagrange Bertrand Le Saux Anne Beaupere Alexandre Boulch Adrien Chan-Hon-Tong Stephane Herbin Hicham Randrianarivo Marin Ferecatu Michal Shimoni Gabriele Moser Devis Tuia
container_volume	9
class	TC1501-1800 QC801-809
format_se	Elektronische Aufsätze
author-letter	Manuel Campos-Taberner
doi_str_mv	10.1109/JSTARS.2016.2569162
author2-role	verfasserin
title_sort	processing of extremely high-resolution lidar and rgb data: outcome of the 2015 ieee grss data fusion contest–part a: 2-d contest
callnumber	TC1501-1800
title_auth	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].
collection_details	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
container_issue	12
title_short	Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest
url	https://doi.org/10.1109/JSTARS.2016.2569162 https://doaj.org/article/bfa02af46c654043b1022ae06944806b https://ieeexplore.ieee.org/document/7536139/ https://doaj.org/toc/2151-1535
remote_bool	true
author2	Adriana Romero-Soriano Carlo Gatta Gustau Camps-Valls Adrien Lagrange Bertrand Le Saux Anne Beaupere Alexandre Boulch Adrien Chan-Hon-Tong Stephane Herbin Hicham Randrianarivo Marin Ferecatu Michal Shimoni Gabriele Moser Devis Tuia
author2Str	Adriana Romero-Soriano Carlo Gatta Gustau Camps-Valls Adrien Lagrange Bertrand Le Saux Anne Beaupere Alexandre Boulch Adrien Chan-Hon-Tong Stephane Herbin Hicham Randrianarivo Marin Ferecatu Michal Shimoni Gabriele Moser Devis Tuia
ppnlink	581732634
callnumber-subject	TC - Hydraulic and Ocean Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/JSTARS.2016.2569162
callnumber-a	TC1501-1800
up_date	2024-07-03T23:59:47.210Z
_version_	1803604393689350144
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ002127431</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503004027.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2016 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/JSTARS.2016.2569162</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ002127431</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJbfa02af46c654043b1022ae06944806b</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TC1501-1800</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC801-809</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Manuel Campos-Taberner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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].</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">extremely high spatial resolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image analysis and data fusion (IADF)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">landcover classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">LiDAR</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multiresolution-</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Ocean engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geophysics. Cosmic physics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Adriana Romero-Soriano</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Carlo Gatta</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gustau Camps-Valls</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Adrien Lagrange</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bertrand Le Saux</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anne Beaupere</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Alexandre Boulch</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Adrien Chan-Hon-Tong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Stephane Herbin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hicham Randrianarivo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Marin Ferecatu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Michal Shimoni</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gabriele Moser</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Devis Tuia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</subfield><subfield code="d">IEEE, 2020</subfield><subfield code="g">9(2016), 12, Seite 5547-5559</subfield><subfield code="w">(DE-627)581732634</subfield><subfield code="w">(DE-600)2457423-5</subfield><subfield code="x">21511535</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:12</subfield><subfield code="g">pages:5547-5559</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/JSTARS.2016.2569162</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/bfa02af46c654043b1022ae06944806b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/7536139/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2151-1535</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2965</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2016</subfield><subfield code="e">12</subfield><subfield code="h">5547-5559</subfield></datafield></record></collection>
score	7.3987026

Nicht das Richtige dabei?

Schreiben Sie uns!

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

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?