Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest
The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Co...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2014-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/6776408/ |
| _version_ | 1819320513016627200 |
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| author | Christian Debes Andreas Merentitis Roel Heremans Jurgen Hahn Nikolaos Frangiadakis Tim van Kasteren Wenzhi Liao Rik Bellens Aleksandra Pizurica Sidharta Gautama Wilfried Philips Saurabh Prasad Qian Du Fabio Pacifici |
| author_facet | Christian Debes Andreas Merentitis Roel Heremans Jurgen Hahn Nikolaos Frangiadakis Tim van Kasteren Wenzhi Liao Rik Bellens Aleksandra Pizurica Sidharta Gautama Wilfried Philips Saurabh Prasad Qian Du Fabio Pacifici |
| author_sort | Christian Debes |
| collection | DOAJ |
| description | The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information. |
| first_indexed | 2024-12-24T11:20:46Z |
| format | Article |
| id | doaj.art-5ccf940bc11d40a28f853a57338c5d2d |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-24T11:20:46Z |
| publishDate | 2014-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-5ccf940bc11d40a28f853a57338c5d2d2022-12-21T16:58:14ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352014-01-01762405241810.1109/JSTARS.2014.23054416776408Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion ContestChristian Debes0Andreas Merentitis1Roel Heremans2Jurgen Hahn3Nikolaos Frangiadakis4Tim van Kasteren5Wenzhi Liao6Rik Bellens7Aleksandra Pizurica8Sidharta Gautama9Wilfried Philips10Saurabh Prasad11Qian Du12Fabio Pacifici13AGT International, Darmstadt, GermanyAGT International, Darmstadt, GermanyAGT International, Darmstadt, GermanyTechnische Universität Darmstadt, Darmstadt, GermanyAGT International, Darmstadt, GermanyAGT International, Darmstadt, GermanyGhent University-iMinds, Ghent, BelgiumGhent University-iMinds, Ghent, BelgiumGhent University-iMinds, Ghent, BelgiumGhent University-iMinds, Ghent, BelgiumGhent University-iMinds, Ghent, BelgiumUniversity of Houston, Houston, TX, USAMississippi State University, Mississipi State, MS, USADigitalGlobe Inc., Longmont, CO, USAThe 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.https://ieeexplore.ieee.org/document/6776408/Data fusionhyperspectralLight Detection And Ranging (LiDAR)multi-modalurbanVHR imagery |
| spellingShingle | Christian Debes Andreas Merentitis Roel Heremans Jurgen Hahn Nikolaos Frangiadakis Tim van Kasteren Wenzhi Liao Rik Bellens Aleksandra Pizurica Sidharta Gautama Wilfried Philips Saurabh Prasad Qian Du Fabio Pacifici Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Data fusion hyperspectral Light Detection And Ranging (LiDAR) multi-modal urban VHR imagery |
| title | Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest |
| title_full | Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest |
| title_fullStr | Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest |
| title_full_unstemmed | Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest |
| title_short | Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest |
| title_sort | hyperspectral and lidar data fusion outcome of the 2013 grss data fusion contest |
| topic | Data fusion hyperspectral Light Detection And Ranging (LiDAR) multi-modal urban VHR imagery |
| url | https://ieeexplore.ieee.org/document/6776408/ |
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