Ship detection is traditionally carried out with patrol ships or aircrafts, with a limited coverage area and also limited by weather conditions. Synthetic aperture radars can surpass these limitations. In this paper, two neural network-based techniques are proposed and compared for detecting ships over a TerraSAR-X image. The first technique is based on a second-order neural network whereas the second one is based on the combination of a Zernike moments-based feature extractor and a Multilayer Perceptron. Good results are obtained with both techniques but the Zernike moments-based one gives rise to the best detection results with less processing time.
High-order neural network-based ship detection algorithms applied to SAR imagery
2012
4 Seiten, 7 Bilder, 13 Quellen
Aufsatz (Konferenz)
Datenträger
Englisch
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