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

Christian Debes, Andreas Merentitis, Roel Heremans, Jürgen Hahn, Nikolaos Frangiadakis, Tim van Kasteren, Wenzhi Liao, Rik Bellens, Aleksandra Pižurica, Sidharta Gautama, Wilfried Philips, Saurabh Prasad, Qian Du, Fabio Pacifici

Research output: Contribution to journalArticlepeer-review

201 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)2405-2418
Number of pages14
JournalIEEE Journal of Selected Topics in Earth Observation and Remote Sensing
Issue number6
Early online date20 Mar 2014
Publication statusPublished - 30 Jun 2014


  • multi-modal
  • urban
  • Light Detection And Ranging (LiDAR)
  • data fusion
  • hyperspectral
  • VHR imagery

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