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.