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

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,...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-02, Vol.14 (3), p.682
Main Authors: Chen, Cheng, Liu, Tao, Liu, Yu, Yang, Bosong, Su, Yi
Format: Article
Language:English
Subjects:
autoencoder
Clutter
clutter mitigation
Decomposition
Dictionaries
Feature extraction
Fourier transforms
holographic subsurface radar
Holography
Image resolution
Learning
Morphology
Object recognition
Optimization
Parameter estimation
Radar
Radar imaging
Remote sensing
Representations
sparse representation
Sparsity
subspace projection
Target detection
Unsupervised learning
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14030682