A Novel Convolutional Autoencoder-Based Clutter Removal Method for Buried Threat Detection in Ground-Penetrating Radar

The clutter encountered in ground-penetrating radar (GPR) systems seriously affects the performance of the subsurface target detection methods. A new clutter removal method based on convolutional autoencoders (CAEs) is introduced. The raw GPR image is encoded via successive convolution and pooling l...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 13
Hlavní autoři: Temlioglu, Eyyup, Erer, Isin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Clutter
Clutter removal
Coefficients
Convolution
convolutional autoencoders (CAEs)
Decoding
Detection
gprMax
Ground penetrating radar
ground-penetrating radar (GPR)
image decomposition
Image reconstruction
Matrix decomposition
Methods
Noise reduction
Radar
Radar detection
Radar imaging
Removal
Simulation
Sparse matrices
Subspace methods
Target detection
ISSN:0196-2892, 1558-0644
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Shrnutí:The clutter encountered in ground-penetrating radar (GPR) systems seriously affects the performance of the subsurface target detection methods. A new clutter removal method based on convolutional autoencoders (CAEs) is introduced. The raw GPR image is encoded via successive convolution and pooling layers and then decoded to provide the clutter-free GPR image. The loss function is defined in terms of the reference clutter-free target image and the decoder output is optimized to learn the weight coefficients from the raw data. The method is compared to the conventional subspace methods, recently proposed nonnegative matrix factorization, as well as low-rank and sparse decomposition (LRSD) methods and dictionary separation-based morphological component analysis. CAE and its deeper version deep CAE (DCAE) are trained by several scenarios generated by the electromagnetic simulation tool gprMax. Simulation results demonstrate the effectiveness of the proposed method for challenging scenarios. While for real GPR image, the simulated data trained networks remain slightly behind the LRSD methods for the dry case, nonetheless, they outperform the aforementioned processing techniques for the more challenging wet case.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3098122