The Data Fusion Technical Committee (DFTC) serves as a global, multi-disciplinary, network for geospatial data fusion, with the aim of connecting people and resources, educating students and professionals, and promoting the best practices in data fusion applications.

The current DFTC Chair and Co-Chair are Fabio Pacifici and QianDu, respectively. The committee
maintains this site and distributes an e-mail newsletter to all committee members on a regular basis.

The DFTC has two main activities:

• organization of a special session held annually during the IGARSS meeting, gathering cutting edge contributions and covering various issues related to data fusion, such as: pan-sharpening, decision fusion, multimodal data fusion, data assimilation, multi-temporal data analysis, ensemble methods, etc.

• organization of the Data Fusion Contest, a scientific challenge held annually since 2006. The Contest is open not only to IEEE members, but to everyone, with the goal of evaluating existing methodologies at the research or operational level to solve remote sensing problems using data from a variety of sensors.

The focus of the 2006 Contest was on the fusion of multispectral and panchromatic images [1]. Six
simulated Pleiades images were provided by the French National Space Agency (CNES). Each data set
included on every high spatial resolution panchromatic image (80 cm) and the corresponding multi-spectral
image (3.2 m resolution). A multi-spectral airborne image was available as ground reference which was
used by the organizing committee for evaluation, but was not distributed to the participants.

In 2007, the Contest theme was urban mapping using synthetic aperture radar (SAR) and optical data, and
9 ERS amplitude data sets and 2 Landsat multi-spectral images were made available [2]. The task was to obtain a classification map as accurate as possible with respect to the unknown (to the participants)
ground reference, depicting land cover and land use patterns for the urban area under study.

The 2008 Contest was dedicated to the classification of very high spatial resolution (1.3 m) hyper-
spectral imagery [3]. The data set was distributed to every participant, and the task was again to obtain
a classification map as accurate as possible with respect to the unknown (to the participants) ground
reference. The data set was collected by the Reflective Optics System Imaging Spectrometer (ROSIS-03) optical sensor with 115 bands covering the 0.43-0.86 μm spectral range.

In 2009-2010, the aim of Contest was to perform change detection using multi-temporal and multi-modal
data [4]. Two pairs of data sets were available over Gloucester, UK, before and after a flood event. The
data set contained SPOT and ERS images (before and after the disaster). The optical and SAR images
were provided by CNES. Similar to previous years’ Contests, the ground truth used to assess the results
was not provided to the participants. Each set of results was tested and ranked a first time using the Kappa
coefficient. The best five results were used to perform decision fusion with majority voting. Then, re-
ranking was carried out after evaluating the level of improvement with respect to the fusion results.

A set of WorldView-2 multi-angular images was provided by DigitalGlobe for the 2011 Contest [5]. This
unique set was composed of five Ortho Ready Standard multi-angular acquisitions, including both 16-
bit panchromatic and multispectral 8-band images. The data were collected over Rio de Janeiro (Brazil) in
January 2010 within a three-minute time frame with satellite elevation angles of 44.7°, 56.0°, and 81.4°
in the forward direction, and 59.8° and 44.6° in the backward direction. Since there was a large variety of
possible applications, each participant was allowed to decide the research topic to work on, exploring the
most creative use of optical multi-angular information. At the end of the Contest, each participant was
asked to submit a paper describing in detail the problem addressed, the method used, and the final result.

[1] L. Alparone, L. Wald, J. Chanussot, C. Thomas, P. Gamba, L. M. Bruce, “Comparison of
pansharpening algorithms: Outcome of the 2006 GRS-S data fusion contest”, IEEE Transactions on
Geoscience and Remote Sensing, vol. 45, no. 10, pp. 3012–3021, Oct. 2007.

[2] F. Pacifici, F. Del Frate, W. J. Emery, P. Gamba, J. Chanussot, “Urban mapping using coarse SAR
and optical data: outcome of the 2007 GRS-S data fusion contest”, IEEE Geoscience and Remote Sensing
Letters, vol. 5, no. 3, pp. 331-335, July 2008

[3] G. Licciardi, F. Pacifici, D. Tuia, S. Prasad, T. West, F. Giacco, J. Inglada, E. Christophe, J.
Chanussot, P. Gamba, “Decision fusion for the classification of hyperspectral data: outcome of the 2008
GRS-S data fusion contest”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 11, pp.
3857-3865, November 2009

[4] N. Longbotham, F. Pacifici, T. Glenn, A. Zare, M. Volpi, D. Tuia, E. Christophe, J. Michel, J.
Inglada, J. Chanussot, Q. Du “Multi-modal Change Detection, Application to the Detection of Flooded
Areas: Outcome of the 2009-2010 Data Fusion Contest”, IEEE Journal of Selected Topics in Applied
Earth Observations and Remote Sensing, vol. 5, no. 1, pp. 331-342, February 2012

[5] F. Pacifici, Q. Du, “Foreword to the Special Issue on Optical Multiangular Data Exploitation and
Outcome of the 2011 GRSS Data Fusion Contest”, IEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, vol. 5, no. 1, pp.3-7, February 2012