Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging

Chen, Cheng; Liu, Tao; Liu, Yu; Yang, Bosong; Su, Yi

doi:10.3390/rs14030682

Open AccessArticle

Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging

by

Cheng Chen

,

Tao Liu

^*,

Yu Liu

,

Bosong Yang

and

Yi Su

College of Electronic Science and Technology, National University of Defense Technology, Changsha 564211, China

^*

Author to whom correspondence should be addressed.

Remote Sens. 2022, 14(3), 682; https://doi.org/10.3390/rs14030682

Submission received: 7 December 2021 / Revised: 17 January 2022 / Accepted: 30 January 2022 / Published: 31 January 2022

(This article belongs to the Special Issue Microwave Imaging of Subsurface Objects in Applications for Engineering Technologies: Instruments and Methods)

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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.

Keywords: clutter mitigation; subspace projection; autoencoder; sparse representation; holographic subsurface radar

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MDPI and ACS Style

Chen, C.; Liu, T.; Liu, Y.; Yang, B.; Su, Y. Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging. Remote Sens. 2022, 14, 682. https://doi.org/10.3390/rs14030682

AMA Style

Chen C, Liu T, Liu Y, Yang B, Su Y. Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging. Remote Sensing. 2022; 14(3):682. https://doi.org/10.3390/rs14030682

Chicago/Turabian Style

Chen, Cheng, Tao Liu, Yu Liu, Bosong Yang, and Yi Su. 2022. "Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging" Remote Sensing 14, no. 3: 682. https://doi.org/10.3390/rs14030682

APA Style

Chen, C., Liu, T., Liu, Y., Yang, B., & Su, Y. (2022). Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging. Remote Sensing, 14(3), 682. https://doi.org/10.3390/rs14030682

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Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging

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