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Automated Processing of Oceanic Bubble Images for measuring Bubble Size Distributions underneath Breaking Waves

Al-Lashi, RS; Gunn, SR; Czerski, H; (2016) Automated Processing of Oceanic Bubble Images for measuring Bubble Size Distributions underneath Breaking Waves. Journal of Atmospheric and Oceanic Technology , 33 (8) pp. 1701-1714. 10.1175/JTECH-D-15-0222.1. Green open access

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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 HiWINGS campaign in the North Atlantic Ocean. The instrument is able to operate autonomously and can continuously capture high resolution images at 15 frames/sec over an 8 hour 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 1 pixel (approximately 20 μm) in radius accurately, has low computing time (approximately 5 seconds per image), and is simple to implement. The algorithm was successfully used to analyse a large number of images (850000 images) from deployment in the North Atlantic Ocean as part of the HiWINGS campaign in 2013.

Type: Article
Title: Automated Processing of Oceanic Bubble Images for measuring Bubble Size Distributions underneath Breaking Waves
Open access status: An open access version is available from UCL Discovery
DOI: 10.1175/JTECH-D-15-0222.1
Publisher version: http://doi.org/10.1175/JTECH-D-15-0222.1
Language: English
Additional information: Copyright © 2016 American Meteorological Society.
Keywords: Observational techniques and algorithms; Algorithms; Data processing; Instrumentation/sensors
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/1502189
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