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- W4319586794 abstract "Synthetic aperture radar (SAR) target recognition faces the challenges that there are limited labeled data. Recent limited data SAR target recognition algorithms overlook the domain knowledge of the target. Domain knowledge enhanced deep neural network (DK-ConvNet) is proposed in this study, which considers the azimuth angle, the length-width-ratio and the targets' area of SAR vehicles, to overcome the over-fitting problems of limited data. The information of vehicle domain knowledge is merged to the probability output of A-ConvNet. To extract the domain knowledge accurately, the segmentation results in the SARBake database are also used. All experiments are conducted under the public moving and stationary target acquisition and recognition (MSTAR) database. Our proposed DK-ConvNet has achieved 72.2% and 93.1% under 10-way 10-shot and 10-way 30-shot in standard operation condition (SOC), whose labeled samples are randomly selected from the training set and DK-ConvNet demonstrated advantages in accuracy compared with other limited data classification methods as well." @default.
- W4319586794 created "2023-02-09" @default.
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- W4319586794 date "2021-12-15" @default.
- W4319586794 modified "2023-09-30" @default.
- W4319586794 title "Domain Knowledge Enhanced Deep Neural Network for Limited Data SAR Vehicle Target Recognition" @default.
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- W4319586794 doi "https://doi.org/10.1109/radar53847.2021.10027975" @default.
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