Advanced search
1 file | 4.25 MB

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

Author
Organization
Project
SBO-IWT project Chameleon: Domain- specific Hyperspectral Imaging Systems for Relevant Industrial Applications
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

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 4.25 MB

Citation

Please use this url to cite or link to this publication:

Chicago
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.” Ed. chanussotJocelyn . Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2405–2418.
APA
Debes, C., Merentitis, A., Heremans, R., Hahn, J., Frangiadakis, N., Kasteren, T., Liao, W., et al. (2014). Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest. (chanussotJocelyn , Ed.)IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 7(6), 2405–2418.
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. chanussotJocelyn , editor. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. 2014;7(6):2405–18.
MLA
Debes, Christian , Andreas Merentitis, Roel Heremans, et al. “Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest.” Ed. chanussotJocelyn . IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 7.6 (2014): 2405–2418. Print.
@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://dx.doi.org/10.1109/JSTARS.2014.2305441},
  volume       = {7},
  year         = {2014},
}

Altmetric
View in Altmetric
Web of Science
Times cited: