Detecting Weather Radar Clutter by Information Fusion With Satellite Images and Numerical Weather Prediction Model Output |
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| Abstract | A 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 %. |
| Type | Conference paper [With referee] |
| Conference | Proceedings of the IEEE Geoscience and Remote Sensing Symposium (IGARSS) 2006 |
| Year | 2006 Month August Vol. I pp. 511-514 |
| Address | Denver, Colorado, USA |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics, Geoinformatics |