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Automated processing of oceanic bubble images for measuring bubble size distribution underneath breaking waves

Al-Lashi, Raied, Gunn, Stephen and Czerski, Helen (2017) Automated processing of oceanic bubble images for measuring bubble size distribution underneath breaking waves Journal of Atmospheric and Oceanic Technology, 33, (8), pp. 1701-1714.

Record type: Article

Abstract

Accurate in situ measurements of oceanic bubble size distributions beneath breaking waves are needed for a better understanding of air–sea gas transfer and aerosol production processes. To achieve this goal, a novel high-resolution optical instrument for imaging oceanic bubbles was designed and built in 2013 for the High Wind Gas Exchange Study (HiWinGS) campaign in the North Atlantic Ocean. The instrument is able to operate autonomously and can continuously capture high-resolution images at 15 frames per second over an 8-h deployment. The large number of images means that it is essential to use an automated processing algorithm to process these images. This paper describes an automated algorithm for processing oceanic images based on a robust feature extraction technique. The main advantages of this robust algorithm are it is significantly less sensitive to the noise and insusceptible to the background changes in illumination, can extract circular bubbles as small as one pixel (approximately 20 ?m) in radius accurately, has low computing time (approximately 5 seconds per image), and is simple to implement. The algorithm was successfully used to analyze a large number of images (850 000 images) from deployment in the North Atlantic Ocean as part of the HiWinGS campaign in 2013.

PDF Automated Processing of Oceanic Bubble Images for measuring Bubble Size_ Distributions underneath Breaking Waves_RA Edit_28Feb1.pdf - Accepted Manuscript
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More information

Accepted/In Press date: 5 May 2016
e-pub ahead of print date: 18 August 2016
Published date: August 2017
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 401047
URI: http://eprints.soton.ac.uk/id/eprint/401047
ISSN: 0739-0572
PURE UUID: 1a339005-ab3d-454f-b8e0-841943dd7483

Catalogue record

Date deposited: 04 Oct 2016 08:47
Last modified: 11 Nov 2017 12:58

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Contributors

Author: Raied Al-Lashi
Author: Stephen Gunn
Author: Helen Czerski

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