Abstract
The holographic subsurface radar (HSR) is an effective remote sensing modality for surveying shallowly buried objects with high resolution images in plan-view. However, strong reflections from the rough surface and inhomogeneities obscure the detection of stationary targets response. In this paper, a learning-based method is proposed to mitigate the clutter in HSR applications. The proposed method first decomposes the HSR image into raw clutter and target data using an adaptive subspace projection approach. Then, the autoencoder is applied to carry out unsupervised learning to extract the target features and mitigate the clutter. The sparse representation is also combined to further optimize the model and the alternating direction multiplier method (ADMM) is used to solve the optimization problem for precision and efficiency. Experiments using real data were conducted to demonstrate that the proposed method can effectively mitigate the strong clutter with the target preserved. The visual and quantitative results show that the proposed method achieves superior performance on suppressing clutter in HSR images compared with the widely used state-of-the-art clutter mitigation approaches.
Highlights
Microwave imaging has been successfully used as a non-destructive remote sensing modality in subsurface targets surveys, including structural assessment [1], landmine detection [2,3,4], and geological exploration [5,6]
Inspired by the success of low rank and sparse matrix representation and autoencoders for the separation of clutter and target response, this paper proposes a learning-based model with subspace projection and sparse representation for Holographic subsurface radar (HSR) clutter mitigation
The proposed clutter mitigation is corroborated on the laboratory data collected with the experimental HSR system developed by our research group
Summary
Microwave imaging has been successfully used as a non-destructive remote sensing modality in subsurface targets surveys, including structural assessment [1], landmine detection [2,3,4], and geological exploration [5,6]. Holographic subsurface radar (HSR) is one such technology that uses electromagnetic waves in a frequency band with a narrow width at several discrete frequencies and employs plane scanning of a surface to record subsurface radar holograms with high resolution in plan-view and with low radar cost [7,8]. The visibility of shallow buried targets in HSR images is usually obscured by clutter contamination, such as surface reflection, antenna coupling, and the inhomogeneities scattering response [9]. One simple and convenient method is the mean subtraction (MS) [10]. MS can be regarded as a filter in the timespace domain by averaging the ensemble of radar data and subtracts the mean to reduce the clutter, but this approach will cause distortion to the intensity of target response
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