Target detection and recognition of ground penetrating radar using morphological image analysis and graph laplacian regularisation

Jun Dong; Vladimir Stankovic; Nigel Davidson

doi:10.1109/SSPD51364.2021.9541516

Target detection and recognition of ground penetrating radar using morphological image analysis and graph laplacian regularisation

Jun Dong, Vladimir Stankovic, Nigel Davidson

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

2 Citations (Scopus)

33 Downloads (Pure)

Abstract

Ground Penetrating Radar (GPR) is often used for detecting non-intrusively buried targets, in road engineering, manufacturing, and in military fields. Based on transmitting high frequency electromagnetic waves, GPR generates high resolution 3D data of the underground structure enabling accurate and fast target detection. However, after inverse
Fourier Transform, the 3D GPR images are often out-of-focus and contain high measurement noise. This calls for advanced signal and image processing methods to improve signal-to-noise ratio, isolate the most discriminative features, and perform
target detection and localisation. Using a vehicle-mounted GPR array data provided in the 2020 UDRC GPR data challenge, we show that morphological image analysis and semi-supervised learning via graph Laplacian regularisation can detect different types of targets buried at various depths with very low false alarm rate.

Original language	English
Title of host publication	2021 Sensor Signal Processing for Defence Conference (SSPD)
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	978-1-6654-3314-3
ISBN (Print)	978-1-6654-3315-0
DOIs	https://doi.org/10.1109/SSPD51364.2021.9541516
Publication status	Published - 23 Sept 2021
Event	International Conference in Sensor Signal Processing for Defence: from Sensor to Decision - Edinburgh, United Kingdom Duration: 14 Sept 2021 → 15 Sept 2021 Conference number: 10 https://sspd.eng.ed.ac.uk

Conference

Conference	International Conference in Sensor Signal Processing for Defence
Abbreviated title	SSPD
Country/Territory	United Kingdom
City	Edinburgh
Period	14/09/21 → 15/09/21
Internet address	https://sspd.eng.ed.ac.uk

Keywords

graph signal processing
ground penetrating radar
image processing

Access to Document

10.1109/SSPD51364.2021.9541516

Dong-etal-ICSSPD-2021-Target-detection-and-recognition-of-ground-penetrating-radar-usingAccepted author manuscript, 260 KB

Cite this

@inproceedings{c4ebb21b7eff439db5753eeaa65478d5,

title = "Target detection and recognition of ground penetrating radar using morphological image analysis and graph laplacian regularisation",

abstract = "Ground Penetrating Radar (GPR) is often used for detecting non-intrusively buried targets, in road engineering, manufacturing, and in military fields. Based on transmitting high frequency electromagnetic waves, GPR generates high resolution 3D data of the underground structure enabling accurate and fast target detection. However, after inverseFourier Transform, the 3D GPR images are often out-of-focus and contain high measurement noise. This calls for advanced signal and image processing methods to improve signal-to-noise ratio, isolate the most discriminative features, and performtarget detection and localisation. Using a vehicle-mounted GPR array data provided in the 2020 UDRC GPR data challenge, we show that morphological image analysis and semi-supervised learning via graph Laplacian regularisation can detect different types of targets buried at various depths with very low false alarm rate.",

keywords = "graph signal processing, ground penetrating radar, image processing",

author = "Jun Dong and Vladimir Stankovic and Nigel Davidson",

year = "2021",

month = sep,

day = "23",

doi = "10.1109/SSPD51364.2021.9541516",

language = "English",

isbn = "978-1-6654-3315-0",

booktitle = "2021 Sensor Signal Processing for Defence Conference (SSPD)",

publisher = "IEEE",

note = "International Conference in Sensor Signal Processing for Defence : from Sensor to Decision, SSPD ; Conference date: 14-09-2021 Through 15-09-2021",

url = "https://sspd.eng.ed.ac.uk",

}

Dong, J , Stankovic, V & Davidson, N 2021, Target detection and recognition of ground penetrating radar using morphological image analysis and graph laplacian regularisation. in 2021 Sensor Signal Processing for Defence Conference (SSPD). IEEE, International Conference in Sensor Signal Processing for Defence, Edinburgh, United Kingdom, 14/09/21. https://doi.org/10.1109/SSPD51364.2021.9541516

TY - GEN

T1 - Target detection and recognition of ground penetrating radar using morphological image analysis and graph laplacian regularisation

AU - Dong, Jun

AU - Stankovic, Vladimir

AU - Davidson, Nigel

N1 - Conference code: 10

PY - 2021/9/23

Y1 - 2021/9/23

N2 - Ground Penetrating Radar (GPR) is often used for detecting non-intrusively buried targets, in road engineering, manufacturing, and in military fields. Based on transmitting high frequency electromagnetic waves, GPR generates high resolution 3D data of the underground structure enabling accurate and fast target detection. However, after inverseFourier Transform, the 3D GPR images are often out-of-focus and contain high measurement noise. This calls for advanced signal and image processing methods to improve signal-to-noise ratio, isolate the most discriminative features, and performtarget detection and localisation. Using a vehicle-mounted GPR array data provided in the 2020 UDRC GPR data challenge, we show that morphological image analysis and semi-supervised learning via graph Laplacian regularisation can detect different types of targets buried at various depths with very low false alarm rate.

AB - Ground Penetrating Radar (GPR) is often used for detecting non-intrusively buried targets, in road engineering, manufacturing, and in military fields. Based on transmitting high frequency electromagnetic waves, GPR generates high resolution 3D data of the underground structure enabling accurate and fast target detection. However, after inverseFourier Transform, the 3D GPR images are often out-of-focus and contain high measurement noise. This calls for advanced signal and image processing methods to improve signal-to-noise ratio, isolate the most discriminative features, and performtarget detection and localisation. Using a vehicle-mounted GPR array data provided in the 2020 UDRC GPR data challenge, we show that morphological image analysis and semi-supervised learning via graph Laplacian regularisation can detect different types of targets buried at various depths with very low false alarm rate.

KW - graph signal processing

KW - ground penetrating radar

KW - image processing

UR - https://sspd.eng.ed.ac.uk/

U2 - 10.1109/SSPD51364.2021.9541516

DO - 10.1109/SSPD51364.2021.9541516

M3 - Conference contribution book

SN - 978-1-6654-3315-0

BT - 2021 Sensor Signal Processing for Defence Conference (SSPD)

PB - IEEE

T2 - International Conference in Sensor Signal Processing for Defence

Y2 - 14 September 2021 through 15 September 2021

ER -

Target detection and recognition of ground penetrating radar using morphological image analysis and graph laplacian regularisation

Abstract

Conference

Keywords

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Signal Processing in the Information Age (UDRC III)

Cite this

Target detection and recognition of ground penetrating radar using morphological image analysis and graph laplacian regularisation

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Signal Processing in the Information Age (UDRC III)

Cite this