Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

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 Co...

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Bibliographic Details
Main Authors: Christian Debes, Andreas Merentitis, Roel Heremans, Jurgen Hahn, Nikolaos Frangiadakis, Tim van Kasteren, Wenzhi Liao, Rik Bellens, Aleksandra Pizurica, Sidharta Gautama, Wilfried Philips, Saurabh Prasad, Qian Du, Fabio Pacifici
Format: Article
Language:English
Published: IEEE 2014-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Data fusion
hyperspectral
Light Detection And Ranging (LiDAR)
multi-modal
urban
VHR imagery
Online Access:https://ieeexplore.ieee.org/document/6776408/
Description
Summary: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.
ISSN:2151-1535

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