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- W2016602926 abstract "In this paper we describe the application of morphological shared-weight neural networks to the problems of classification and detection of vehicles in synthetic aperture radar (SAR). Classification experiments were carried out with SAR images of T72 tanks and armored personnel carriers. A correct classification rate of more than 98% was achieved on a testing data set. Detection experiments were carried out with T72 tanks embedded in SAR images of clutter scenes. A near perfect detection rate and a low false alarm rate were achieved. The data used in the experiments was the standard training and testing MSTAR data set collected by Sandia National Laboratory." @default.
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- W2016602926 date "1998-09-15" @default.
- W2016602926 modified "2023-09-23" @default.
- W2016602926 title "<title>Detection and classification of MSTAR objects via morphological shared-weight neural networks</title>" @default.
- W2016602926 doi "https://doi.org/10.1117/12.321856" @default.
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