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- W4313137075 abstract "With the continuous development of Deep Learning (DL), ship detection in SAR (Synthetic Aperture Radar) images based on convolutional neural networks (CNN) has become a common approach. CNNs with complex structures have achieved good performance in SAR images, but face challenges such as high time consumption and high false alarm rate, because of the sparsity of ships in the remote sensing images. In this paper, a rotated ship detection network based on the CFAR (Constant False Alarm Rate) preprocessing is proposed to address these problems. It first uses a CFAR preprocessing to fast narrow down the scope of detection so as to save processing time. Then, a classification network is designed to reduce the false alarm rate. The experiment results based on the Gaofen-3(GF-3) dataset show that the proposed method can reduce the false alarm rate greatly and use much less CPU time." @default.
- W4313137075 created "2023-01-06" @default.
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- W4313137075 creator A5063972152 @default.
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- W4313137075 date "2022-07-17" @default.
- W4313137075 modified "2023-09-27" @default.
- W4313137075 title "Sar Ship Detection Network Incorporating CFAR Preprocessing" @default.
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- W4313137075 doi "https://doi.org/10.1109/igarss46834.2022.9884868" @default.
- W4313137075 hasPublicationYear "2022" @default.
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