Discriminating Underwater LiDAR Target Signatures Using Sparse Multi-Spectral Depth Codes

Puneet Chhabra, Aurora Maccarone, Aongus McCarthy, Gerald Buller, Andrew Wallace

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The analysis and discrimination of underwater multi-spectral full-waveform LiDAR signatures acquired using a single-photon counting sensor is presented. We use a realistic scaled exemplar of a marine environment, with known and unknown targets, and show how we can both discriminate different materials and detect and locate mines. Each waveform is a temporal photon histogram whose inherent nature changes with the laser wavelength, target geometry and environment. Discriminatory dictionaries for target materials and mine types are learnt by making multi-spectral measurements. An accuracy of 97.8% and 98.7% was achieved for material and mine type discrimination, respectively.

Original languageEnglish
Title of host publication2016 Sensor Signal Processing for Defence (SSPD)
PublisherIEEE
ISBN (Electronic)9781509003266
DOIs
Publication statusPublished - 18 Oct 2016
Event6th Conference of the Sensor Signal Processing for Defence 2016 - Edinburgh, United Kingdom
Duration: 22 Sep 201623 Sep 2016

Conference

Conference6th Conference of the Sensor Signal Processing for Defence 2016
CountryUnited Kingdom
CityEdinburgh
Period22/09/1623/09/16

Keywords

  • ATR
  • Dictionary learning
  • Full-waveform
  • Lidar
  • Multispectral
  • Photon counting
  • Target discrimination

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics
  • Instrumentation
  • Artificial Intelligence

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