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• W4386640998 abstract "Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To address this issue, a novel aircraft detection method is proposed for low SCNR SAR images that is based on coherent scattering enhancement and a fusion attention mechanism. Considering the scattering characteristics discrepancy between human-made targets and natural background, a coherent scattering enhancement technique is introduced to heighten the aircraft scatter information and suppress the clutter and speckle noise. This is beneficial for the later ability of the deep neural network to extract accurate and discriminative semantic information about the aircraft. Further, an improved Faster R-CNN is developed with a novel pyramid network constructed by fusing local and contextual attention. The local attention adaptively highlights the significant objects by enhancing their distinguishable features, and the contextual attention facilitates the network to extract distinct contextual information of the image. Fusing the local and contextual attention can guarantee that the aircraft is detected as completely as possible. Extensive experiments are performed on TerraSAR-X SAR datasets for benchmark comparison. The experimental results demonstrate that the proposed aircraft detection approach could achieve up to 91.7% of average precision in low SCNR, showing effectiveness and superiority over a number of benchmarks." @default.
• W4386640998 created "2023-09-13" @default.
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• W4386640998 date "2023-09-12" @default.
• W4386640998 modified "2023-10-18" @default.
• W4386640998 title "Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid" @default.
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• W4386640998 doi "https://doi.org/10.3390/rs15184480" @default.
• W4386640998 hasPublicationYear "2023" @default.
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