Tor Vergata University, Rome, Italy
GEOINFORMATION PhD PROGRAMME

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The 2009 Joint Urban Remote Sensing Event, recently held Shanghai, China, has awarded Fabio Pacifici, a third year student of the GeoInformation Doctorate at the Tor Vergata University, with the Best Student Paper in recognition of his contribution entitled "Pulse Coupled Neural Networks for Detecting Urban Areas Changes at Very High Resolutions", co-authored by Fabio Del Frate and William J. Emery. This is the third international award obtained by Fabio Pacifici, already winner of the First prize of the 2008 and 2007 IEEE Geoscience and Remote Sensing Data Fusion Contest.

Abstract— The development of fully automatic change detection procedures for very high resolution images is not a trivial task as several issues have to be considered. The crucial ones include possible different viewing angles, mis-registrations, shadow and other seasonal and meteorological effects which add up and combine to reduce the attainable accuracy in the change detection results. However this challenge has to be faced to fully exploit the big potential offered by the ever-increasing amount of information made available by ongoing and future satellite missions. A novel approach based on Pulse-Coupled Neural Networks (PCNNs) for image change detection is presented. PCNNs are based on the implementation of the mechanisms underlying the visual cortex of small mammals and with respect to more traditional neural networks architectures own interesting advantages. In particular, they are unsupervised and context sensitive. The performance of the algorithm has been evaluated on very high resolution QuickBird and WorldView-1 images. Qualitative and more quantitative results are discussed.