Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest

The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2009-11, Vol.47 (11), p.3857-3865
Main Authors: Licciardi, G., Pacifici, F., Tuia, D., Prasad, S., West, T., Giacco, F., Thiel, C., Inglada, J., Christophe, E., Chanussot, J., Gamba, P.
Format: Article
Language:English
Subjects:
Algorithms
Applied geophysics
Classification
Collaboration
Computer Science
Data fusion
Data integration
decision fusion
Earth sciences
Earth, ocean, space
Exact sciences and technology
Geoscience and remote sensing
hyperspectral imagery
Hyperspectral imaging
Hyperspectral sensors
Image Processing
Internal geophysics
Laser radar
Neural networks
Optical imaging
Optical sensors
Remote sensing
Stressing
Support vector machines
Urban areas
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Summary:The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2009.2029340