Detection of anomalous propagation echoes in weather radar data using neural networks
The authors investigate a neural network-based methodology for detection of the anomalous propagation (AP) radar echo. The methodology is devised to cope with the situations when only single scan data are available. The output of the procedure is quantified in four classes corresponding to the upper...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 1999-01, Vol.37 (1), p.287-296 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Grecu, M. Krajewski, W.F. |
| description | The authors investigate a neural network-based methodology for detection of the anomalous propagation (AP) radar echo. The methodology is devised to cope with the situations when only single scan data are available. The output of the procedure is quantified in four classes corresponding to the upper limits of 25, 50, 75, and 100% of AP echo per scan. The high dimension of the input data space is reduced by feature extraction based on physical considerations. Fractal based, statistical, and wavelet analyses are performed, and their characteristics are used as features. A feedforward neural network is used for classification in the four classes, with a fuzzy strategy used in the network training. The authors test the methodology on real data and make a comprehensive assessment of the procedure's accuracy based on cross validation. |
| doi_str_mv | 10.1109/36.739163 |
| format | Article |
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| ispartof | IEEE transactions on geoscience and remote sensing, 1999-01, Vol.37 (1), p.287-296 |
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| language | eng |
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| source | IEEE Xplore |
| subjects | Applied geophysics Earth sciences Earth, ocean, space Exact sciences and technology Feature extraction Feedforward neural networks Fractals Fuzzy neural networks Internal geophysics Meteorological radar Neural networks Performance analysis Radar detection Testing Wavelet analysis |
| title | Detection of anomalous propagation echoes in weather radar data using neural networks |
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