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Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest

Christian Debes, Andreas Merentitis, Roel Heremans, Jürgen Hahn, Nikolaos Frangiadakis, Tim Kasteren, Wenzhi Liao (UGent) , Rik Bellens, Aleksandra Pizurica (UGent) , Sidharta Gautama (UGent) , et al.
(2014) IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. 7(6). p.2405-2418
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Abstract
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.
Keywords
multi-modal, urban, Light Detection And Ranging (LiDAR), Data fusion, hyperspectral, VHR imagery, TRANSFORMATION, AREAS, ALGORITHMS, CLASSIFICATION, RANDOM FOREST, DIRECTIONAL MORPHOLOGICAL PROFILES, REMOTE-SENSING IMAGES, TARGET RECOGNITION, DECISION FUSION, FEATURE-EXTRACTION

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MLA
Debes, Christian, et al. “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, edited by Jocelyn chanussot, vol. 7, no. 6, 2014, pp. 2405–18, doi:10.1109/JSTARS.2014.2305441.
APA
Debes, C., Merentitis, A., Heremans, R., Hahn, J., Frangiadakis, N., Kasteren, T., … Pacifici, F. (2014). 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, 7(6), 2405–2418. https://doi.org/10.1109/JSTARS.2014.2305441
Chicago author-date
Debes, Christian, Andreas Merentitis, Roel Heremans, Jürgen Hahn, Nikolaos Frangiadakis, Tim Kasteren, Wenzhi Liao, et al. 2014. “Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest.” Edited by Jocelyn chanussot. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 7 (6): 2405–18. https://doi.org/10.1109/JSTARS.2014.2305441.
Chicago author-date (all authors)
Debes, Christian, Andreas Merentitis, Roel Heremans, Jürgen Hahn, Nikolaos Frangiadakis, Tim Kasteren, Wenzhi Liao, Rik Bellens, Aleksandra Pizurica, Sidharta Gautama, Wilfried Philips, Saurabh Prasad, Qian Du, and Fabio Pacifici. 2014. “Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest.” Ed by. Jocelyn chanussot. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 7 (6): 2405–2418. doi:10.1109/JSTARS.2014.2305441.
Vancouver
1.
Debes C, Merentitis A, Heremans R, Hahn J, Frangiadakis N, Kasteren T, et al. Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest. chanussot J, editor. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. 2014;7(6):2405–18.
IEEE
[1]
C. Debes et al., “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, vol. 7, no. 6, pp. 2405–2418, 2014.
@article{5827183,
  abstract     = {{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.}},
  author       = {{Debes, Christian and Merentitis, Andreas and Heremans, Roel and Hahn, Jürgen and Frangiadakis, Nikolaos and Kasteren, Tim and Liao, Wenzhi and Bellens, Rik and Pizurica, Aleksandra and Gautama, Sidharta and Philips, Wilfried and Prasad, Saurabh and Du, Qian and Pacifici, Fabio}},
  editor       = {{chanussot, Jocelyn}},
  issn         = {{1939-1404}},
  journal      = {{IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING}},
  keywords     = {{multi-modal,urban,Light Detection And Ranging (LiDAR),Data fusion,hyperspectral,VHR imagery,TRANSFORMATION,AREAS,ALGORITHMS,CLASSIFICATION,RANDOM FOREST,DIRECTIONAL MORPHOLOGICAL PROFILES,REMOTE-SENSING IMAGES,TARGET RECOGNITION,DECISION FUSION,FEATURE-EXTRACTION}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{2405--2418}},
  title        = {{Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest}},
  url          = {{http://doi.org/10.1109/JSTARS.2014.2305441}},
  volume       = {{7}},
  year         = {{2014}},
}
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