Yujun Zhang*, Dezhi Han, Peng Chen
CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2657-2675, 2023, DOI:10.32604/cmc.2023.042311
Abstract Synthetic Aperture Radar (SAR) image target detection has widespread applications in both military and civil domains. However, SAR images pose challenges due to strong scattering, indistinct edge contours, multi-scale representation, sparsity, and severe background interference, which make the existing target detection methods in low accuracy. To address this issue, this paper proposes a multi-scale fusion framework (Swin-PAFF) for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure (FPN). Firstly, to tackle the issue of inadequate perceptual image context information in SAR target detection, we… More >
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