Tor Vergata GeoInformaion PhD Student Fabio Pacifici ranked First Place of the 2007 IEEE Data Fusion Contest organized by the Data Fusion Technical Committee of the Geoscience and Remote Sensing Society.

 

The Data Fusion Contest has been organized by the Data Fusion Technical Committee (DFTC) of the Geoscience and Remote Sensing Society (GRS-S) of the IEEE. In 2007, the contest was related to urban mapping using radar and optical data. A set of satellite radar and optical images (ERS amplitude data and Landsat multi-spectral data) was made available with the task of obtaining a classified map as accurate as possible relative to ground reference data depicting land cover and land use classes for the urban area of interest.
The winning algorithm was based on a neural network approach. One of the major advantages of neural networks with respect to statistically based classifiers is that NNs draw their own input-output discriminant relations directly from the data and do not require that a particular form of a probability density function be assumed. The classification procedure was divided into three steps: preprocessing, neural network classification and post-processing.
The final classification map, shown in figure, reached the accuracy of 0.9393 in terms of k-coeff. with respect to the unknown ground reference data used to rank the contest’s results.

 

 


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