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

Jun Dong, Vladimir Stankovic, Nigel Davidson

Research output: Contribution to conferencePaperpeer-review

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 languageEnglish
Number of pages5
Publication statusAccepted/In press - 1 Jul 2021
EventInternational Conference in Sensor Signal Processing for Defence: from Sensor to Decision - Edinburgh, United Kingdom
Duration: 14 Sep 202115 Sep 2021
Conference number: 10
https://sspd.eng.ed.ac.uk

Conference

ConferenceInternational Conference in Sensor Signal Processing for Defence
Abbreviated titleSSPD
Country/TerritoryUnited Kingdom
CityEdinburgh
Period14/09/2115/09/21
Internet address

Keywords

  • graph signal processing
  • ground penetrating radar
  • image processing

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