<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pacifici, F.</style></author><author><style face="normal" font="default" size="100%">Del Frate, F.</style></author><author><style face="normal" font="default" size="100%">Emery, W.J.</style></author><author><style face="normal" font="default" size="100%">Gamba, P.</style></author><author><style face="normal" font="default" size="100%">Chanussot, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Urban Mapping Using Coarse SAR and Optical Data: Outcome of the 2007 GRSS Data Fusion Contest</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Geoscience and Remote Sensing Letters</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">AD 2007</style></keyword><keyword><style  face="normal" font="default" size="100%">coarse optical data</style></keyword><keyword><style  face="normal" font="default" size="100%">coarse SAR data</style></keyword><keyword><style  face="normal" font="default" size="100%">data fusion</style></keyword><keyword><style  face="normal" font="default" size="100%">European Remote Sensing satellite (ERS)</style></keyword><keyword><style  face="normal" font="default" size="100%">geophysical signal processing</style></keyword><keyword><style  face="normal" font="default" size="100%">GRSS Data Fusion Contest</style></keyword><keyword><style  face="normal" font="default" size="100%">IEEE Geoscience and Remote Sensing Data Fusion Technical Committee</style></keyword><keyword><style  face="normal" font="default" size="100%">image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">land use-land cover map extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Landsat</style></keyword><keyword><style  face="normal" font="default" size="100%">mapping accuracy</style></keyword><keyword><style  face="normal" font="default" size="100%">multisource coarse resolution data set</style></keyword><keyword><style  face="normal" font="default" size="100%">multitemporal resolution data set</style></keyword><keyword><style  face="normal" font="default" size="100%">neural nets</style></keyword><keyword><style  face="normal" font="default" size="100%">neural networks (NNs)</style></keyword><keyword><style  face="normal" font="default" size="100%">pre-postprocessing enhanced neural classification</style></keyword><keyword><style  face="normal" font="default" size="100%">satellite sensors</style></keyword><keyword><style  face="normal" font="default" size="100%">sensor fusion</style></keyword><keyword><style  face="normal" font="default" size="100%">spaceborne radar</style></keyword><keyword><style  face="normal" font="default" size="100%">synthetic aperture radar</style></keyword><keyword><style  face="normal" font="default" size="100%">terrain mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">urban mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">urban remote sensing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">331 - 335</style></pages><isbn><style face="normal" font="default" size="100%">1545-598X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The 2007 data fusion contest that was organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee was dealing with the extraction of a land use/land cover maps in and around an urban area, exploiting multitemporal and multisource coarse-resolution data sets. In particular, synthetic aperture radar and optical data from satellite sensors were considered. Excellent indicators for mapping accuracy were obtained by the top teams. The best algorithm is based on a neural classification enhanced by preprocessing and postprocessing steps.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>