Automatic Detection of Underwater Objects in Sonar Imagery

Avi Abu; Roee DIamant

doi:10.1109/OCEANSE.2019.8867489

Automatic Detection of Underwater Objects in Sonar Imagery

Avi Abu, Roee DIamant

Department of Marine Technologies

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

Abstract

This paper introduces a new unsupervised statistically-based algorithm for the detection of underwater objects in sonar imagery. Highlights are detected by a higher-order-statistics representation of the image followed by a segmentation process to form a region-of-interest (ROI). Our algorithm sets its main parameters in situ and avoids the need of parameter calibration. Moreover, we do not require knowledge about the target's shape or size, thereby making our algorithm robust to any sonar detection application. Results obtained from real sonar system show a good trade-off between probability of detection and false alarm rate (FAR).

Original language	English
Title of host publication	OCEANS 2019 - Marseille, OCEANS Marseille 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728114507
DOIs	https://doi.org/10.1109/OCEANSE.2019.8867489
State	Published - Jun 2019
Event	2019 OCEANS - Marseille, OCEANS Marseille 2019 - Marseille, France Duration: 17 Jun 2019 → 20 Jun 2019

Publication series

Name	OCEANS 2019 - Marseille, OCEANS Marseille 2019
Volume	2019-June

Conference

Conference	2019 OCEANS - Marseille, OCEANS Marseille 2019
Country/Territory	France
City	Marseille
Period	17/06/19 → 20/06/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Sonar image processing
binary hypothesis testing
detection in sonar imagery
highlight detection
image segmentation
likelihood ratio test

ASJC Scopus subject areas

Oceanography
Automotive Engineering
Management, Monitoring, Policy and Law
Water Science and Technology
Instrumentation

Access to Document

10.1109/OCEANSE.2019.8867489

Cite this

@inproceedings{74191b417cf54672b25830ea19ba7ce1,

title = "Automatic Detection of Underwater Objects in Sonar Imagery",

abstract = "This paper introduces a new unsupervised statistically-based algorithm for the detection of underwater objects in sonar imagery. Highlights are detected by a higher-order-statistics representation of the image followed by a segmentation process to form a region-of-interest (ROI). Our algorithm sets its main parameters in situ and avoids the need of parameter calibration. Moreover, we do not require knowledge about the target's shape or size, thereby making our algorithm robust to any sonar detection application. Results obtained from real sonar system show a good trade-off between probability of detection and false alarm rate (FAR).",

keywords = "Sonar image processing, binary hypothesis testing, detection in sonar imagery, highlight detection, image segmentation, likelihood ratio test",

author = "Avi Abu and Roee DIamant",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 OCEANS - Marseille, OCEANS Marseille 2019 ; Conference date: 17-06-2019 Through 20-06-2019",

year = "2019",

month = jun,

doi = "10.1109/OCEANSE.2019.8867489",

language = "English",

series = "OCEANS 2019 - Marseille, OCEANS Marseille 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "OCEANS 2019 - Marseille, OCEANS Marseille 2019",

address = "United States",

}

Abu, A & DIamant, R 2019, Automatic Detection of Underwater Objects in Sonar Imagery. in OCEANS 2019 - Marseille, OCEANS Marseille 2019., 8867489, OCEANS 2019 - Marseille, OCEANS Marseille 2019, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., 2019 OCEANS - Marseille, OCEANS Marseille 2019, Marseille, France, 17/06/19. https://doi.org/10.1109/OCEANSE.2019.8867489

Automatic Detection of Underwater Objects in Sonar Imagery. / Abu, Avi ; DIamant, Roee.
OCEANS 2019 - Marseille, OCEANS Marseille 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8867489 (OCEANS 2019 - Marseille, OCEANS Marseille 2019; Vol. 2019-June).

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

TY - GEN

T1 - Automatic Detection of Underwater Objects in Sonar Imagery

AU - Abu, Avi

AU - DIamant, Roee

PY - 2019/6

Y1 - 2019/6

N2 - This paper introduces a new unsupervised statistically-based algorithm for the detection of underwater objects in sonar imagery. Highlights are detected by a higher-order-statistics representation of the image followed by a segmentation process to form a region-of-interest (ROI). Our algorithm sets its main parameters in situ and avoids the need of parameter calibration. Moreover, we do not require knowledge about the target's shape or size, thereby making our algorithm robust to any sonar detection application. Results obtained from real sonar system show a good trade-off between probability of detection and false alarm rate (FAR).

AB - This paper introduces a new unsupervised statistically-based algorithm for the detection of underwater objects in sonar imagery. Highlights are detected by a higher-order-statistics representation of the image followed by a segmentation process to form a region-of-interest (ROI). Our algorithm sets its main parameters in situ and avoids the need of parameter calibration. Moreover, we do not require knowledge about the target's shape or size, thereby making our algorithm robust to any sonar detection application. Results obtained from real sonar system show a good trade-off between probability of detection and false alarm rate (FAR).

KW - Sonar image processing

KW - binary hypothesis testing

KW - detection in sonar imagery

KW - highlight detection

KW - image segmentation

KW - likelihood ratio test

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U2 - 10.1109/OCEANSE.2019.8867489

DO - 10.1109/OCEANSE.2019.8867489

M3 - Conference contribution

AN - SCOPUS:85103855141

T3 - OCEANS 2019 - Marseille, OCEANS Marseille 2019

BT - OCEANS 2019 - Marseille, OCEANS Marseille 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 OCEANS - Marseille, OCEANS Marseille 2019

Y2 - 17 June 2019 through 20 June 2019

ER -

Automatic Detection of Underwater Objects in Sonar Imagery

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this