Adaptive Subsurface 3-D Imaging Based on Peak Phase-Retrieval and Complex-Valued Self-Organizing Map

We propose an adaptive subsurface 3-D visualization system based on a complex-valued self-organizing map (CSOM). Conventionally buried things can be detected in the so-called B-scan images obtained by a ground penetrating radar. In contrast, our proposed method is able not only to detect their prese...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2020-01, Vol.17 (1), p.52-56
Main Authors: Shimomura, Soshi, Hirose, Akira
Format: Article
Language:English
Subjects:
Adaptive systems
Amplitude
Amplitudes
Coefficients
Complex-valued self-organizing map (CSOM)
cubic scan (C-scan)
Directive antennas
Frequency-domain analysis
Ground penetrating radar
ground penetrating radar (GPR)
Image contrast
Image detection
Imaging techniques
Landmine detection
Network topologies
Phase retrieval
Radar
Scattering
Scattering coefficients
Self organizing maps
Self-organizing feature maps
Time-domain analysis
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Description
Summary:We propose an adaptive subsurface 3-D visualization system based on a complex-valued self-organizing map (CSOM). Conventionally buried things can be detected in the so-called B-scan images obtained by a ground penetrating radar. In contrast, our proposed method is able not only to detect their presence but also to classify the targets by the self-organizing dynamics in the CSOM. Instead of utilizing only the amplitude information in the time domain, we use both the amplitude and the phase information to obtain the scattering coefficients of scatterers by use of the phase retrieval method.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2915256