The low-frequency Synthetic Aperture Radar (SAR) can penetrate foliage and detect the targets concealed under the foliage, while the high-frequency SAR cannot. The low-frequency SAR image and high-frequency SAR image cannot be directly compared to detect the foliage-penetrating (FOPEN) targets due to their distinct statistical properties. This paper presents an FOPEN target detection method based on bi-frequency SAR and conditional generative adversarial networks (CGAN). The high-frequency SAR image is translated into low-frequency one by the CGAN. The direct comparison between the real and generated low-frequency SAR images is used to detect the FOPEN targets. Experiments on P- and Ku-band SAR images show that our method performs better than the double-parameter constant false alarm rate detection (DP-CFAR) using the single P-band SAR image.
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