Detecting Weather Radar Clutter by Information Fusion With Satellite Images and Numerical Weather Prediction Model Output

AbstractA method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect information on precipitation in the atmosphere from Meteosat-8 multispectral images and near-surface temperature estimates from the DMI-HIRLAM-S05 numerical weather prediction model. Alternatively, an operational nowcasting product called 'Precipitating Clouds' based on Meteosat-8 input is used. A scale-space ensemble method is used for classification and the clutter detection method is illustrated on a case of severe sea clutter contaminated radar data. Detection accuracies above 90 % are achieved and using an ensemble classification method the error rate is reduced by 40 %.
TypeConference paper [With referee]
ConferenceProceedings of the IEEE Geoscience and Remote Sensing Symposium (IGARSS) 2006
Year2006    Month August    Vol. I    pp. 511-514
AddressDenver, Colorado, USA
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics, Geoinformatics