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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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 13
Main Authors:	Temlioglu, Eyyup, Erer, Isin
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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