The holographic subsurface radar (HSR) has been a promising geophysical electromagnetic technique for detecting shallowly buried targets with high lateral resolution image. However, radar images are considerably interpreted by strong reflections from rough surface and inhomogeneity in media of interest. In this article, we focus on mitigating the clutter in HSR applications using a learning-based approach, which requires neither prior information regarding the penetrable medium characteristics nor analytic framework to describe the through-medium interference. The generative adversarial network (GAN) with attentive subspace projection is developed to remove the clutter and recover the target image. The subspaces containing target response are selected with the multihead attention preliminarily. Then, the generative network will further focus on the target regions, and the discriminative network will assess the generated results locally and globally. Experiments using real data were conducted to demonstrate the effectiveness of our approach. The visual and quantitative results show that the proposed approach achieves superior performance on removing clutter in HSR images compared with the state-of-the-art clutter mitigation approaches.

中文翻译：

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使用具有细心子空间投影的生成对抗网络减轻全息地下雷达成像中的杂波

全息地下雷达（HSR）是一种很有前途的地球物理电磁技术，可用于探测具有高横向分辨率图像的浅埋目标。然而，雷达图像被粗糙表面的强烈反射和感兴趣介质的不均匀性所解释。在本文中，我们专注于使用基于学习的方法减轻 HSR 应用程序中的混乱，该方法既不需要关于可穿透介质特性的先验信息，也不需要分析框架来描述穿透介质干扰。开发了具有细心子空间投影的生成对抗网络 (GAN) 以消除杂波并恢复目标图像。通过多头注意力初步选择包含目标响应的子空间。然后，生成网络将进一步关注目标区域，判别网络将在本地和全球范围内评估生成的结果。使用真实数据进行的实验证明了我们方法的有效性。视觉和定量结果表明，与最先进的杂波缓解方法相比，所提出的方法在去除 HSR 图像中的杂波方面取得了卓越的性能。