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- W4386869613 abstract "Intelligent ship detection algorithms for synthetic aperture radar (SAR) images have achieved significant results in Earth observation applications. By learning features such as scale, shape and texture from samples, they can quickly locate and recognize ships in complex backgrounds. However, due to the lack of use of polarization features, the upper bound of detection performance is still limited, especially under poor image quality conditions such as ambiguous interference. To solve this, the dual-polarization image feature fusion network (DPFF-Net) is proposed. The key of it lies in adaptive mining, enhancement and fusion of polarization features through the designed siamese structure, polarization-aware enhancement block (PAEB) and dynamic gated fusion block (DGFB). With fully utilizing complementary information hidden between co-polarization and cross-polarization data, more comprehensive and accurate features are obtained and used as the detect head input. Thus, the proposed algorithm achieves state-of-the-art performance, and its effectiveness are validated by experiments on dual-polarization SAR datasets." @default.
- W4386869613 created "2023-09-20" @default.
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- W4386869613 date "2023-01-01" @default.
- W4386869613 modified "2023-10-18" @default.
- W4386869613 title "DPFF-Net: Dual-Polarization Image Feature Fusion Network for SAR Ship Detection" @default.
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- W4386869613 doi "https://doi.org/10.1109/tgrs.2023.3317143" @default.
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