TY - JOUR
T1 - Hyperspectral and LiDAR data fusion
T2 - outcome of the 2013 GRSS data fusion contest
AU - Debes, Christian
AU - Merentitis, Andreas
AU - Heremans, Roel
AU - Hahn, Jürgen
AU - Frangiadakis, Nikolaos
AU - van Kasteren, Tim
AU - Liao, Wenzhi
AU - Bellens, Rik
AU - Pižurica, Aleksandra
AU - Gautama, Sidharta
AU - Philips, Wilfried
AU - Prasad, Saurabh
AU - Du, Qian
AU - Pacifici, Fabio
PY - 2014/6/30
Y1 - 2014/6/30
N2 - 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.
AB - 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.
KW - multi-modal
KW - urban
KW - Light Detection And Ranging (LiDAR)
KW - data fusion
KW - hyperspectral
KW - VHR imagery
U2 - 10.1109/JSTARS.2014.2305441
DO - 10.1109/JSTARS.2014.2305441
M3 - Article
SN - 1939-1404
VL - 7
SP - 2405
EP - 2418
JO - IEEE Journal of Selected Topics in Earth Observation and Remote Sensing
JF - IEEE Journal of Selected Topics in Earth Observation and Remote Sensing
IS - 6
ER -