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

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

doi:10.1109/SSPD.2016.7590595

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

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

^*Corresponding author for this work

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

13 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 language	English
Title of host publication	2016 Sensor Signal Processing for Defence (SSPD)
Publisher	IEEE
ISBN (Electronic)	9781509003266
DOIs	https://doi.org/10.1109/SSPD.2016.7590595
Publication status	Published - 18 Oct 2016
Event	6th Conference of the Sensor Signal Processing for Defence 2016 - Edinburgh, United Kingdom Duration: 22 Sept 2016 → 23 Sept 2016

Conference

Conference	6th Conference of the Sensor Signal Processing for Defence 2016
Country/Territory	United Kingdom
City	Edinburgh
Period	22/09/16 → 23/09/16

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SSPD.2016.7590595

Cite this

@inproceedings{6eeb4256285b40d589f48383d07bf379,

title = "Discriminating Underwater LiDAR Target Signatures Using Sparse Multi-Spectral Depth Codes",

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.",

keywords = "ATR, Dictionary learning, Full-waveform, Lidar, Multispectral, Photon counting, Target discrimination",

author = "Puneet Chhabra and Aurora Maccarone and Aongus McCarthy and Gerald Buller and Andrew Wallace",

year = "2016",

month = oct,

day = "18",

doi = "10.1109/SSPD.2016.7590595",

language = "English",

booktitle = "2016 Sensor Signal Processing for Defence (SSPD)",

publisher = "IEEE",

address = "United States",

note = "6th Conference of the Sensor Signal Processing for Defence 2016 ; Conference date: 22-09-2016 Through 23-09-2016",

}

Chhabra, P, Maccarone, A , McCarthy, A , Buller, G & Wallace, A 2016, Discriminating Underwater LiDAR Target Signatures Using Sparse Multi-Spectral Depth Codes. in 2016 Sensor Signal Processing for Defence (SSPD)., 7590595, IEEE, 6th Conference of the Sensor Signal Processing for Defence 2016, Edinburgh, United Kingdom, 22/09/16. https://doi.org/10.1109/SSPD.2016.7590595

TY - GEN

T1 - Discriminating Underwater LiDAR Target Signatures Using Sparse Multi-Spectral Depth Codes

AU - Chhabra, Puneet

AU - Maccarone, Aurora

AU - McCarthy, Aongus

AU - Buller, Gerald

AU - Wallace, Andrew

PY - 2016/10/18

Y1 - 2016/10/18

N2 - 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.

AB - 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.

KW - ATR

KW - Dictionary learning

KW - Full-waveform

KW - Lidar

KW - Multispectral

KW - Photon counting

KW - Target discrimination

UR - http://www.scopus.com/inward/record.url?scp=85009810429&partnerID=8YFLogxK

U2 - 10.1109/SSPD.2016.7590595

DO - 10.1109/SSPD.2016.7590595

M3 - Conference contribution

AN - SCOPUS:85009810429

BT - 2016 Sensor Signal Processing for Defence (SSPD)

PB - IEEE

T2 - 6th Conference of the Sensor Signal Processing for Defence 2016

Y2 - 22 September 2016 through 23 September 2016

ER -

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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