Title :
Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest
Author :
Debes, Christian ; Merentitis, A. ; Heremans, Roel ; Hahn, Juergen ; Frangiadakis, Nikolaos ; van Kasteren, Tim ; Wenzhi Liao ; Bellens, Rik ; Pizurica, Aleksandra ; Gautama, Sidharta ; Philips, Wilfried ; Prasad, Santasriya ; Qian Du ; Pacifici, F.
Author_Institution :
AGT Int., Darmstadt, Germany
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 :
geophysical image processing; graph theory; hyperspectral imaging; image classification; learning (artificial intelligence); remote sensing by radar; 2013 GRSS Data Fusion Contest; LIDAR derived digital surface model; airborne laser mapping; graph-based method; hyperspectral-LIDAR data fusion; unsupervised-supervised classification scheme; Data integration; Feature extraction; Hyperspectral imaging; Laser radar; Vegetation mapping; Data fusion; Light Detection And Ranging (LiDAR); VHR imagery; hyperspectral; multi-modal; urban;
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
DOI :
10.1109/JSTARS.2014.2305441
